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Journal Cover Swarm and Evolutionary Computation
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   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [3031 journals]
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: April 2017
      Source:Swarm and Evolutionary Computation, Volume 33

      PubDate: 2017-03-02T00:54:25Z
  • Service Allocation in the Cloud Environments using Multi-Objective
           Particle Swarm Optimization Algorithm based on Crowding Distance
    • Authors: Nima Jafari Navimipour; Fereshteh Eslamic
      Abstract: Publication date: Available online 27 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Nima Jafari Navimipour, Fereshteh Eslamic
      Cloud computing is an emerging Internet-based computing paradigm, with its built-in elasticity and scalability. In cloud computing field, a service provider offers a large number of resources like computing units, storage space, and software for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Therefore, service allocation is one of the most challenging issues in the cloud environments. The problem of service allocation in the cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. This paper considers three conflicting objectives, namely maximizing revenue for users and providers as well as finding the optimal solution at desired time. We use a Multi-Objective Particle Swarm Optimization based on Crowding Distance (MOPSO-CD) to solve the problem because MOPSO-CD is highly competitive in converging towards the Pareto front and generates a well-distributed set of non-dominated solutions. In addition, fuzzy set theory is employed to specify the best compromise solution. We simulate the proposed method using Matlab and compare the performance of the method against the performance of two other multi-objective algorithms, in order to prove that the proposed method is highly competitive with respect to them. Finally, the experiments results show that the method improves the speed of the execution of the resources allocation algorithm while generating high revenue for both the users and the providers and increasing the resource utilization.

      PubDate: 2017-03-02T00:54:25Z
      DOI: 10.1016/j.swevo.2017.02.007
  • Bird mating optimizer for structural damage detection using a hybrid
           objective function
    • Authors: J.J. Zhu; M. Huang; Z.R. Lu
      Abstract: Publication date: Available online 24 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): J.J. Zhu, M. Huang, Z.R. Lu
      A structural damage detection approach based on bird mating optimizer (BMO) in time-frequency domain is proposed in this paper. A hybrid objective function is introduced by minimizing the discrepancies between the measured and calculated natural frequencies and correlation function vector of acceleration of damaged and intact structures. Then the BMO algorithm with a disturbance procedure is developed to solve the objective function. Benefited from the hybrid objective function, only a few number of natural frequencies are needed in the detection process. And the disturbance procedure designed in this paper can enhance the precision of identification. The efficiency and robustness of the proposed method are verified by a planar truss and a frame, a three connected shear buildings and an experimental work. The studies in numerical simulations validate that the proposed objective function and disturbance procedure are helpful to improve the precision of identification. The experimental work shows that the proposed method has the potential of practical application. In addition, comparison among the proposed method and other optimization algorithms, i.e. GA, ABC, L-SHADE and HCLPSO, reveals the superiority of the proposed method in structural damage detection.

      PubDate: 2017-03-02T00:54:25Z
      DOI: 10.1016/j.swevo.2017.02.006
  • Multi-objective two-level swarm intelligence approach for multiple RNA
           sequence-structure alignment
    • Authors: Soniya Lalwani; Rajesh Kumar; Kusum Deep
      Abstract: Publication date: Available online 22 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Soniya Lalwani, Rajesh Kumar, Kusum Deep
      This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.

      PubDate: 2017-02-23T00:23:13Z
      DOI: 10.1016/j.swevo.2017.02.002
  • PV Cell and Module Efficient Parameters Estimation Using Evaporation Rate
           based Water Cycle Algorithm
    • Authors: Dhruv Kler; Pallavi Sharma; Ashish Banerjee; K.P.S. Rana; Vineet Kumar
      Abstract: Publication date: Available online 21 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Dhruv Kler, Pallavi Sharma, Ashish Banerjee, K.P.S. Rana, Vineet Kumar
      In order to carry out precise performance investigations and control studies on photovoltaic (PV) systems an accurate model is always desired. In this work, a new and powerful metaheuristic optimization technique known as Evaporation Rate based Water Cycle Algorithm (ER-WCA) has been explored for effective parameters estimation of PV cell/module. Single and double diode based models of PV cell and single diode based model of PV module have been successfully identified from their respective single I-V non-linear characteristics and the modeling performance of ER-WCA, assessed in terms of root mean square error, mean absolute error and mean relative error, between computed and experimental data, is found to be superior to the several recent prominent published works particularly the modeling of a single diode based PV module. Furthermore, the PV module modeling capability of ER-WCA under varying temperature and irradiation conditions is also analysed and it is found to be effective proving its practical applications. Based on the presented detailed investigation it is concluded that ER-WCA is a promising optimization technique for PV cell/module identification.

      PubDate: 2017-02-23T00:23:13Z
      DOI: 10.1016/j.swevo.2017.02.005
  • An efficient side lobe reduction technique considering mutual coupling
           effect in linear array antenna using BAT algorithm
    • Authors: Avishek Das; D. Mandal; S.P. Ghoshal; R. Kar
      Abstract: Publication date: Available online 17 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Avishek Das, D. Mandal, S.P. Ghoshal, R. Kar
      This paper presents an investigation of mutual coupling effect among the array elements in a symmetric linear array antenna with the aim of reducing the side lobe level and the null control for the radiation pattern synthesis using BAT Algorithm. PSO and DE optimization techniques are also adopted for the sake of comparison and to prove the superiority of BAT algorithm based design. Reduced side lobe level and null control, with and without considering the mutual coupling effect in the cost function have been achieved by an optimum perturbation of the array elements' current excitation amplitude weights and the inter-element spacing among the array elements. The results are also compared with those of a uniform reference array having equal number of elements with λ 2 inter-element spacing. The approach proposed in this paper is a generic one and can be easily applied to any type of symmetrical linear arrays having any number of elements. Five different design examples are presented and their performances are studied to illustrate the capability of BAT algorithm based approach over those of PSO and DE.

      PubDate: 2017-02-23T00:23:13Z
      DOI: 10.1016/j.swevo.2017.02.004
  • A Feasible Method for Controlled Intentional Islanding in Microgrids Based
           on PSO algorithm
    • Authors: M.H. Oboudi; R. Hooshmand; A. Karamad
      Abstract: Publication date: Available online 14 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): M.H. Oboudi, R. Hooshmand, A. Karamad
      Intentional islanding is a suitable approach to increase the system reliability in situations where the electrical connection between the microgrid and the upstream network is lost. In this paper, an innovated two-stage method for the intentional islanding process in microgrids is proposed. The important and practical issues such as the load controllability; load priorities; voltage and the line capacity constraints; reduction of problem solution space and ability to make larger islands are taken into account. In the first stage, the problem is relaxed by considering the problem as an optimization problem known as Tree Knapsack Problem (TKP) solved by Particle Swarm Optimization (PSO). In the second stage, the power flow is calculated and the constraints are verified, then adjusting measures will be taken. The proposed method is conducted on the IEEE 69-bus test system with 6 DGs and the results are compared with other methods. Moreover, for real time verification, the obtained results are simulated by DIgSILENT/Power Factory software package. The simulation results suggest that the proposed algorithm is a valid method for the intentional islanding process.

      PubDate: 2017-02-15T23:34:41Z
      DOI: 10.1016/j.swevo.2017.02.003
  • Particle Swarm Clustering Fitness Evaluation with Computational Centroids
    • Authors: Jenni Raitoharju; Kaveh Samiee; Serkan Kiranyaz; Moncef Gabbouj
      Abstract: Publication date: Available online 9 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jenni Raitoharju, Kaveh Samiee, Serkan Kiranyaz, Moncef Gabbouj
      In this paper, we propose a new way to carry out fitness evaluation in dynamic Particle Swarm Clustering (PSC) with centroid-based encoding. Generally, the PSC fitness function is selected among the clustering validity indices and most of them directly depend on the cluster centroids. In the traditional fitness evaluation approach, the cluster centroids are replaced by the centroids proposed by a particle position. We propose to first compute the centroids of the corresponding clusters and then use these computational centroids in fitness evaluation. The proposed way is called Fitness Evaluation with Computational Centroids (FECC). We conducted an extensive set of comparative evaluations and the results show that FECC leads to a clear improvement in clustering results compared to the traditional fitness evaluation approach with most of the fitness functions considered in this study. The proposed approach was found especially beneficial when underclustering is a problem. Furthermore, we evaluated 31 fitness functions based on 17 clustering validity indices using two PSC methods over a large number of synthetic and real data sets with varying properties. We used three different performance criteria to evaluate the clustering quality and found out that the top three fitness functions are Xu index, WB index, and Dunn variant DU 23 applied using FECC. These fitness functions were consistently performing well for both PSC methods, for all data distributions, and according to all performance criteria. In all test cases, they were clearly among the better half of the fitness functions and, in the majority of the cases, they were among the top 4 functions. Further guidance for improved fitness function selection in different situations is provided in the paper.

      PubDate: 2017-02-09T23:10:48Z
      DOI: 10.1016/j.swevo.2017.01.003
  • Success Rates Analysis of Three Hybrid Algorithms on SAT Instances
    • Authors: Xinsheng Lai; Yuren Zhou
      Abstract: Publication date: Available online 9 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Xinsheng Lai, Yuren Zhou
      In recent years, combining different individual heuristics to construct hybrid algorithms seems to be a promising way for designing more powerful algorithms. We are interested in when a certain termination criterion is met, whether the success (referring to finding a globally optimal solution) rate of a hybrid algorithm can be better than that of the individual algorithms on which the hybrid algorithm is based or not. In this paper, we concentrate on rigorously analyzing the success rate of hybrid algorithms. This makes a step into theoretical understanding of hybrid algorithms, which lags far behind empirical investigations. We derive the formulas for calculating the success rates of three hybrid algorithms by making use of a Markov chain. These three hybrid algorithms are based on different ways of combining two individual heuristics. As an application of these formulas, we then investigate the relationships between the success rate curves of RandomWalk, Local (1+1) EA (evolutionary algorithm) and that of three hybrid algorithms based on different ways of combining the two heuristics for solving two satisfiability (SAT) problem instances. The computational success rate curves are validated by experimental ones. Meanwhile, we discuss the relationship between success rate and time complexity.

      PubDate: 2017-02-09T23:10:48Z
      DOI: 10.1016/j.swevo.2017.02.001
  • On The Use of Two Reference Points in Decomposition Based Multiobjective
           Evolutionary Algorithms
    • Authors: Zhenkun Wang; Qingfu Zhang; Hui Li; Hisao Ishibuchi; Licheng Jiao
      Abstract: Publication date: Available online 24 January 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Zhenkun Wang, Qingfu Zhang, Hui Li, Hisao Ishibuchi, Licheng Jiao
      Decomposition based multiobjective evolutionary algorithms approximate the Pareto front of a multiobjective optimization problem by optimizing a set of subproblems in a collaborative manner. Often, each subproblem is associated with a direction vector and a reference point. The settings of these parameters have a very critical impact on convergence and diversity of the algorithm. Some work has been done to study how to set and adjust direction vectors to enhance algorithm performance for particular problems. In contrast, little effort has been made to study how to use reference points for controlling diversity in decomposition based algorithms. In this paper, we first study the impact of the reference point setting on selection in decomposition based algorithms. To balance the diversity and convergence, a new variant of the multiobjective evolutionary algorithm based on decomposition with both the ideal point and the nadir point is then proposed. This new variant also employs an improved global replacement strategy for performance enhancement. Comparison of our proposed algorithm with some other state-of-the-art algorithms is conducted on a set of multiobjective test problems. Experimental results show that our proposed algorithm is promising.

      PubDate: 2017-01-29T08:55:12Z
      DOI: 10.1016/j.swevo.2017.01.002
  • Comments on “Evolutionary and GPU computing for topology
           optimization of structures”
    • Authors: David Guirguis
      Abstract: Publication date: Available online 18 January 2017
      Source:Swarm and Evolutionary Computation
      Author(s): David Guirguis

      PubDate: 2017-01-21T18:11:07Z
      DOI: 10.1016/j.swevo.2017.01.001
  • A survey of swarm intelligence for dynamic optimization: algorithms and
    • Authors: Michalis Mavrovouniotis; Changhe Li; Shengxiang Yang
      Abstract: Publication date: Available online 11 January 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Michalis Mavrovouniotis, Changhe Li, Shengxiang Yang
      Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constraint, multi-objective and classification, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

      PubDate: 2017-01-14T17:42:35Z
      DOI: 10.1016/j.swevo.2016.12.005
  • Time series analysis and short-term forecasting of solar irradiation, a
           new hybrid approach
    • Authors: N. Bigdeli; M. Salehi Borujeni; K. Afshar
      Abstract: Publication date: Available online 29 December 2016
      Source:Swarm and Evolutionary Computation
      Author(s): N. Bigdeli, M. Salehi Borujeni, K. Afshar
      In this paper, nonlinear time series analysis and short-term prediction of solar irradiation were considered, simultaneously. The proposed methodology is to employ time series analysis methods as well as swarm and evolutionary algorithms in conjunction with well-known regression, fuzzy and neural network model structures to develop a simple but efficient and applicable model for solar irradiation forecasting. The employed experimental data was the hourly solar irradiation of Qazvin city in Iran for five years. At first, the solar irradiation data was normalized using the daily clear sky irradiation data which is an annually periodic time series. Then, the properties of normalized solar irradiation were characterized via time series analysis methods such as recurrence plots, autocorrelation and mutual information analysis. Based on these analyses, each year was divided into two seasons, the sunny and cloudy seasons which are noticeably different in dynamics. Next, a hybrid but simple model was developed to predict the solar irradiation in different seasons. For the sunny season, an optimized multivariate regression model was proposed; and for the cloudy season a bi-level model consisting of an optimized regression model and ANFIS was developed. The model parameters were tuned optimally by various evolutionary algorithms being GA, PSO, ABC, COA, and flower pollination algorithm (FPA). A Fourier-type model was also developed for modeling of the clear sky data. The results showed the out-performance of FPA method in tuning of the model parameters and convergence time. Besides, the performance of the proposed bi-level model was evaluated in comparison with some other model structures such as artificial neural networks, ANFIS networks, LSE-regression models, LS-support vector machines model, etc. The results showed that the proposed method performs considerably better than the other methods in forecasting the solar irradiation time series in both sunny and cloudy seasons.

      PubDate: 2017-01-05T16:38:06Z
      DOI: 10.1016/j.swevo.2016.12.004
  • Node-depth phylogenetic-based encoding, a spanning-tree representation for
           evolutionary algorithms. part I: Proposal and properties analysis
    • Authors: Telma Woerle de Lima; Alexandre Cláudio Botazzo Delbem; Anderson da Silva Soares; Fernando Marques Federson; João Bosco Augusto London Junior; Jeffrey Van Baalen
      Pages: 1 - 10
      Abstract: Publication date: December 2016
      Source:Swarm and Evolutionary Computation, Volume 31
      Author(s): Telma Woerle de Lima, Alexandre Cláudio Botazzo Delbem, Anderson da Silva Soares, Fernando Marques Federson, João Bosco Augusto London Junior, Jeffrey Van Baalen
      Representation choice and the development of search operators are crucial aspects of the efficiency of Evolutionary Algorithms (EAs) in combinatorial problems. Several researchers have proposed representations and operators for EAs that manipulate spanning trees. This paper proposes a new encoding called Node-depth Phylogenetic-based Encoding (NPE). NPE represents spanning trees by the relation between nodes and their depths using a relatively simple codification/decodification process. The proposed NPE operators are based on methods used for tree rearrangement in phylogenetic tree reconstruction: subtree prune and regraft; and tree bisection and reconstruction. NPE and its operators are designed to have high locality, feasibility, low time complexity, be unbiased, and have independent weight. Therefore, NPE is a good choice of data structure for EAs applied to network design problems.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.05.001
      Issue No: Vol. 31 (2016)
  • Maintaining regularity and generalization in data using the minimum
           description length principle and genetic algorithm: Case of grammatical
    • Authors: Hari Mohan Pandey; Ankit Chaudhary; Deepti Mehrotra; Graham Kendall
      Pages: 11 - 23
      Abstract: Publication date: December 2016
      Source:Swarm and Evolutionary Computation, Volume 31
      Author(s): Hari Mohan Pandey, Ankit Chaudhary, Deepti Mehrotra, Graham Kendall
      In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length principle. Statistical tests demonstrate the superiority of the proposed algorithm. Overall, the proposed GAWMDL algorithm greatly improves the performance in three main aspects: maintains regularity of the data, alleviates premature convergence and is capable in grammatical inference from both positive and negative corpora.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.05.002
      Issue No: Vol. 31 (2016)
  • Hybrid HSA and PSO algorithm for energy efficient cluster head selection
           in wireless sensor networks
    • Authors: T. Shankar; S. Shanmugavel; A. Rajesh
      Pages: 1 - 10
      Abstract: Publication date: October 2016
      Source:Swarm and Evolutionary Computation, Volume 30
      Author(s): T. Shankar, S. Shanmugavel, A. Rajesh
      Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm.

      PubDate: 2016-09-25T09:40:37Z
      DOI: 10.1016/j.swevo.2016.03.003
      Issue No: Vol. 30 (2016)
  • Fuzzy evolutionary cellular learning automata model for text summarization
    • Authors: Razieh Abbasi-ghalehtaki; Hassan Khotanlou; Mansour Esmaeilpour
      Pages: 11 - 26
      Abstract: Publication date: October 2016
      Source:Swarm and Evolutionary Computation, Volume 30
      Author(s): Razieh Abbasi-ghalehtaki, Hassan Khotanlou, Mansour Esmaeilpour
      Text summarization is the automatic process of creating a short form of an original text. The main goal of an automatic text summarization system is production of a summary which satisfies the user's needs. In this paper, a new model for automatic text summarization is introduced which is based on fuzzy logic system, evolutionary algorithms and cellular learning automata. First, the most important features including word features, similarity measure, and the position and the length of a sentence are extracted. A linear combination of these features shows the importance of each sentence. To calculate similarity measure, a combined method based on artificial bee colony algorithm and cellular learning automata are used. In this method, joint n-grams among sentences are extracted by cellular learning automata and then an artificial bee colony algorithm classifies n-friends in order to extract data and optimize the similarity measure as fitness function. Moreover, a new approach is proposed to adjust the best weights of the text features using particle swarm optimization and genetic algorithm. This method discovers more important and less important text features and then assigns fair weights to them. At last, a fuzzy logic system is used to perform the final scoring. The results of the proposed approach were compared with the other methods including Msword, System19, System21, System28, System31, FSDH, FEOM, NetSum, CRF, SVM, DE, MA-SingleDocSum, Unified Rank and Manifold Ranking using ROUGE-l and ROUGE-2 measures on the DUC2002 dataset. The results show that proposed method outperforms the aforementioned methods.

      PubDate: 2016-09-25T09:40:37Z
      DOI: 10.1016/j.swevo.2016.03.004
      Issue No: Vol. 30 (2016)
  • An object tracking method using modified galaxy-based search algorithm
    • Authors: Faegheh Sardari; Mohsen Ebrahimi Moghaddam
      Pages: 27 - 38
      Abstract: Publication date: Available online 21 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Faegheh Sardari, Mohsen Ebrahimi Moghaddam
      Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in this field of study. In this paper, we have proposed an object tracking method based on a meta-heuristic approach. Although there are some meta-heuristic approaches in the literature, we have modified GbSA (galaxy based search algorithm) which is more precise than related works. The GbSA searches the state space by simulating the movement of the spiral galaxy to find the optimum object state. The proposed method searches each frame of video with particle filter and the MGSbA in a similar manner. It receives current frame and the temporal information that is related to previous frames as input and tries to find the optimum object state in each one. The experimental results show the efficiency of this algorithm in comparison with results of related methods.

      PubDate: 2016-04-26T22:54:15Z
      DOI: 10.1016/j.swevo.2016.04.001
      Issue No: Vol. 30 (2016)
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: February 2017
      Source:Swarm and Evolutionary Computation, Volume 32

      PubDate: 2016-12-26T14:11:44Z
  • A Fast Hypervolume Driven Selection Mechanism for Many-Objective
           Optimisation Problems
    • Authors: Shahin Rostami; Ferrante Neri
      Abstract: Publication date: Available online 24 December 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Shahin Rostami, Ferrante Neri
      Solutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a “many-objective optimisation problem”. A problem of this category introduces many challenges, one of which is the effective and efficient selection of optimal solutions. The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an effective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a non-dominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computational cost. The present study proposes a novel hypervolume driven selection mechanism for many-objective problems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indicator for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-off between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world efficiency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.

      PubDate: 2016-12-26T14:11:44Z
      DOI: 10.1016/j.swevo.2016.12.002
  • A novel teaching-learning based optimization approach for design of
           broad-band anti-reflection coatings
    • Authors: Sanjaykumar J. Patel; Aditi Toshniwal; Vipul Kheraj
      Abstract: Publication date: Available online 23 December 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Sanjaykumar J. Patel, Aditi Toshniwal, Vipul Kheraj
      The design of thin-film multilayered anti-reflection (AR) coating is quite an intricate task due to highly nonlinear and complex dimensional search space, which includes many local minima. In this paper, a novel teaching-learning based optimization (TLBO) approach is employed to design ultra-low reflective coating over a broad wavelength-band using multilayer thin-film structures for optoelectronic devices. The algorithm is implemented using LabVIEW as a programming tool. Various design specific input parameters such as scanning range of wavelengths, step-size, angle of incidence, number of layers, the name and sequence of coating materials etc. are required to be fed by the user on the graphical user interface. The algorithm minimizes the average reflectivity computed over given wavelength range by tuning the thickness of layers in the multilayer stack. The reliability and evolution of design solution with iterations have been systematically investigated for different learner-sizes. Finally, using the optimized learner size and desired number of iterations, the optimum AR design is obtained in terms of the thickness of each layer for the multilayer AR coating. The effectiveness of the TLBO approach has been compared with that of an established algorithm, i.e. genetic algorithm (GA), by means of Wilcoxon singed ranked test. It is concluded that the TLBO can be a very efficient, simpler and relatively faster approach to address complex optimization problems such as broad-band AR coating designs.

      PubDate: 2016-12-26T14:11:44Z
      DOI: 10.1016/j.swevo.2016.12.003
  • hGRGA: A Scalable Genetic Algorithm using Homologous Gene Schema
    • Authors: Sumaiya Iqbal; Md Tamjidul Hoque
      Abstract: Publication date: Available online 9 December 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Sumaiya Iqbal, Md Tamjidul Hoque
      In this article, we propose a new evolutionary algorithm, referred as homologous Gene Replacement Genetic Algorithm (hGRGA) that includes a novel and generic operator called homologous Gene Replacement (hGR). The hGR operator improves the chromosomes in gene level to promote their overall functionality. The hGRGA effectively encodes the key idea of the natural evolutionary process that locates and utilizes good local schema present in the genes of a chromosome through hGR operator. The proposed hGRGA is evaluated and compared with two variants of GA and two other state-of-the-art evolutionary computing algorithms based on widely-used benchmark functions with a motivation to apply to wider varieties of optimization problems. The simulation results show that the new algorithm can offer faster convergence and better precision while finding optima. Our analysis shows that hGR is effectively a scalable operator that makes hGRGA well suited for real world problems with increasing size and complexity.

      PubDate: 2016-12-11T12:29:08Z
      DOI: 10.1016/j.swevo.2016.12.001
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: December 2016
      Source:Swarm and Evolutionary Computation, Volume 31

      PubDate: 2016-11-26T16:48:49Z
  • Termite spatial correlation based particle swarm optimization for
           unconstrained optimization
    • Authors: Avinash Sharma; Rajesh Kumar; B.K. Panigrahi; Swagatam Das
      Abstract: Publication date: Available online 21 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Avinash Sharma, Rajesh Kumar, B.K. Panigrahi, Swagatam Das
      In last few years, swarm intelligence has become the mainstay in the field of continuous optimization with many researchers developing algorithms simulating swarm behavior for the purpose of numerical optimization. This work proposes a new Termite Spatial Correlation based Particle Swarm Optimization (TSC-PSO) algorithm inspired by the movement strategy shown within Termites (Cornitermes cumulans). TSC-PSO modifies the velocity equation in the original PSO algorithm by replicating the step correlation based termite motion mechanism that exhibits individually in nature and works with decentralized control to collectively perform the overall task. Further, the algorithm incorporates the mutation strategy within it to make it suitable to avoid stagnation conditions while performing optimization in complex search spaces. For deriving its utility various benchmark functions of different geometric properties have been used. Experiments clearly demonstrate the success of the proposed algorithm in different benchmark conditions against various state-of-the-art optimization algorithms.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.11.001
  • Rocchio algorithm-based particle initialization mechanism for effective
           PSO classification of high dimensional data
    • Authors: Anwar Ali Yahya; Addin Osman; Mohammad Said El-Bashir
      Abstract: Publication date: Available online 21 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anwar Ali Yahya, Addin Osman, Mohammad Said El-Bashir
      In recent years, there has been a growing interest in applying Particle Swarm Optimization (PSO) to data classification. Nonetheless, due to the curse of dimensionality, the effectiveness of the PSO applied to high dimensional data classification becomes questionable. This paper proposes a novel specialized PSO initialization mechanism, developed specifically for PSO applications to high dimensional data classification. The proposed initialization mechanism is inspired by the center-based sampling theory, which argues that the center of the search space is a promising region for the initialization step in evolutionary algorithms. Furthermore, the proposed initialization mechanism is based on an information retrieval algorithm called Rocchio Algorithm (RA); that identifies the center region of the search space of data classification. To validate the proposed mechanism, RA-based PSO has been applied to a high dimensional classification task in educational data mining. More specifically, RA-based PSO has been applied to classify a dataset of teachers' classroom questions into Bloom's taxonomy cognitive levels. To do so, a dataset of teachers' classroom questions has been collected and annotated manually with Bloom's taxonomy cognitive levels. Pre-processing steps have been applied to convert questions into a representation suitable for classification. Using this dataset, the standard PSO, PSO with generic initialization mechanisms, and RA-based PSO have been experimented and compared. The results show a poor performance of the standard PSO and the PSO with the generic initialization mechanisms, as well as a significant improvement in the performance of RA-based PSO. These results indicate that a proper task-specific PSO initialization mechanism is crucial for effective PSO performance in high dimensional data classification. Furthermore, a comparison between RA-based PSO and pure RA classification provide a quantitative estimation of the role of initialization mechanism and PSO search for the classification of the dataset. On the other hand, the comparison between RA-based PSO approach and three conventional machine learning approaches, experimented on the same dataset confirms the effectiveness of RA-based PSO for high dimensional data classification. Moreover, the comparison between RA-based PSO approach and machine learning approaches, in terms of computational time efficiency, shows that they are comparable in classification time. However, as the learning of PSO is a time-consuming process, its effectiveness is significantly affected if the learning time is a matter.

      PubDate: 2016-11-26T16:48:49Z
      DOI: 10.1016/j.swevo.2016.11.005
  • A production inventory model with price discounted fuzzy demand using an
           interval compared hybrid algorithm
    • Authors: Anindita Kundu; Partha Guchhait; Prasenjit Pramanik; Manas Kumar Maiti; Manoranjan Maiti
      Abstract: Publication date: Available online 19 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anindita Kundu, Partha Guchhait, Prasenjit Pramanik, Manas Kumar Maiti, Manoranjan Maiti
      An economic production quantity (EPQ) model in an imprecise environment is proposed, where the production rate, planning horizon and demand coefficients are fuzzy in nature. At the beginning of each cycle a price discount is offered for a period to boost the demand. During this period, demand increases with time depending upon the amount of discount. Here, demand also depends on the unit selling price. After withdrawal of the price discount, demand depends only on the unit selling price. The governing differential equation for the model is obviously fuzzy in nature as the production rate and demand are fuzzy. For this reason, the model is formulated using a fuzzy differential equation and the α-cut of the total profit from the planning horizon is obtained using fuzzy Riemann integration. To optimize the interval objective function, using a fuzzy preference relation on intervals and a fuzzy possibility/necessity measure, a hybrid algorithm with varying population size is developed by combining the features of particle swarm optimization (PSO) and a genetic algorithm (GA). This algorithm is named Interval Compared Hybrid Particle Swarm-Genetic Algorithm (ICHPSGA) and is used to find an optimal decision for the decision maker (DM) in different cases of the model. To test the efficiency of the algorithm, it is compared with two other established algorithms namely PSO and PSGA. Numerical experiments are performed to illustrate the model and some interesting observations are made.

      PubDate: 2016-11-19T14:40:26Z
      DOI: 10.1016/j.swevo.2016.11.004
  • Portfolio optimization using novel co-variance guided Artificial Bee
           Colony algorithm
    • Authors: Divya Kumar; K.K. Mishra
      Abstract: Publication date: Available online 18 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Divya Kumar, K.K. Mishra
      Although the use of evolutionary algorithms and fuzzy logic for portfolio optimization is an established research area, this field remains fascinating because of its important financial aspects. The field is brisk and it trances as there always remain research issues which are yet to explore. The problem of portfolio optimization comprises of finding an optimal distribution of funds among various available securities so as to maximize the return and minimize the risk. Artificial Bee Colony (ABC) is one of the effectual and widely used optimization technique based on swarm intelligence. Mixing co-variance principles with ABC algorithm assists in quick convergence with more precision. This paper presents a novel co-variance guided Artificial Bee Colony algorithm for portfolio optimization. As portfolio optimization consists of simultaneous optimization of multiple conflicting objectives, this algorithm is named as Multi-objective Co-variance based ABC (M-CABC). The efficacy of the proposed algorithm is tested on benchmark problems of portfolio optimization from the OR-library. The results validate the adept performance of the proposed algorithm in finding various optimal trade-off solutions simultaneously handling realistic constraints. The article concludes with exhaustive post-result analysis and observatory remarks to bring out some of the crucial properties of optimal portfolios.

      PubDate: 2016-11-19T14:40:26Z
      DOI: 10.1016/j.swevo.2016.11.003
  • Noisy Evolutionary Optimization Algorithms-A Comprehensive Survey
    • Authors: Pratyusha Rakshit; Amit Konar; Swagatam Das
      Abstract: Publication date: Available online 18 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Pratyusha Rakshit, Amit Konar, Swagatam Das
      Noisy optimization is currently receiving increasing popularity for its widespread applications in engineering optimization problems, where the objective functions are often found to be contaminated with noisy sensory measurements. In absence of knowledge of the noise-statistics, discriminating better trial solutions from the rest becomes difficult in the “selection” step of an evolutionary optimization algorithm with noisy objective/s. This paper provides a thorough survey of the present state-of-the-art research on noisy evolutionary algorithms for both single and multi-objective optimization problems. This is undertaken by incorporating one or more of the five strategies in traditional evolutionary algorithms. The strategies include i) fitness sampling of individual trial solution, ii) fitness estimation of noisy samples, iii) dynamic population sizing over the generations, iv) adaptation of the evolutionary search strategy, and v) modification in the selection strategy.

      PubDate: 2016-11-19T14:40:26Z
      DOI: 10.1016/j.swevo.2016.09.002
  • A novel evolutionary rigid body docking algorithm for medical image
    • Authors: Rutuparna Panda; Sanjay Agrawal; Madhusmita Sahoo; Rajashree Nayak
      Abstract: Publication date: Available online 9 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Rutuparna Panda, Sanjay Agrawal, Madhusmita Sahoo, Rajashree Nayak
      One of today's motivating medical image processing problem is registration. Medical images acquired from different modalities give rise to a practical problem in image registration. For many years, mutual information has been utilized as a similarity criterion for registration. Therefore, it is believed to be the state-of-the-art in this field. Nonetheless, it is understood to be non-convex and possess many local maxima. To overcome this problem, we introduce a novel evolutionary rigid body docking (ERBD) algorithm for medical image registration. Here, the ligand is taken as the non-aligned (target) image (to be registered) and protein as the reference image. The alignment of the target image is changed as per the optimal configuration. This paper introduces a firsthand objective (fitness) function for finding optimal configurations. Genetic algorithm (GA) is used to optimize the fitness function. The proposed method is useful for recovery of rotation and translation parameters. Different image data formats like magnetic resonance image (MRI), computed tomography (CT) and positron emission tomography (PET) images from Retrospective Image Registration Evaluation (RIRE) project are used to demonstrate the effectiveness of our proposed method. Experimental results are presented to reveal the fact that our approach seems to be efficient.

      PubDate: 2016-11-13T14:13:29Z
      DOI: 10.1016/j.swevo.2016.11.002
  • An Elitist Nondominated Sorting Genetic Algorithm for QoS Multicast
           Routing in Wireless Networks
    • Authors: Zaheeruddin; D.K. Lobiyal; Sunita Prasad
      Abstract: Publication date: Available online 4 November 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Zaheeruddin, D.K. Lobiyal, Sunita Prasad
      Due to the increasing popularity of real time multimedia applications, Quality-of-Service (QoS) based multicast routing has emerged as an active area of research. The fundamental requirements of many multimedia applications are cost minimization and bounded end-to-end delay. In addition, video data traffic is sensitive to packet loss and delay variance. Hence, multiobjective optimization seems to be the most appropriate method for such complex problems. We, therefore, formulate QoS based multicast routing as a multiobjective optimization problem using Elitist Nondominated Sorting Genetic Algorithm (NSGA-II). To enhance the performance of NSGA-II, we propose a new encoding scheme that aims to achieve a diversified solution set and faster convergence of search towards optimal Pareto front. It has also been observed that identical solutions cause loss of diversity which degrades the performance of NSGA-II algorithm. To overcome this drawback, the second enhancement based on replacement strategy is used. In this approach, one copy of identical solution is retained and new random solutions are introduced in the population to obtain a well distributed Pareto front. The results of new encoding scheme and replacement strategy are compared with other existing evolutionary multiobjective algorithms to demonstrate the effectiveness of the proposed approach. To further strengthen the usefulness of modified algorithm, the experimental results are validated using statistical significance tests.

      PubDate: 2016-11-05T13:32:45Z
      DOI: 10.1016/j.swevo.2016.10.004
  • On the application of search-based techniques for software engineering
           predictive modeling: A systematic review and future directions
    • Authors: Ruchika Malhotra; Megha Khanna; Rajeev R. Raje
      Abstract: Publication date: Available online 20 October 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Ruchika Malhotra, Megha Khanna, Rajeev R. Raje
      Software engineering predictive modeling involves construction of models, with the help of software metrics, for estimating quality attributes. Recently, the use of search-based techniques have gained importance as they help the developers and project-managers in the identification of optimal solutions for developing effective prediction models. In this paper, we perform a systematic review of 78 primary studies from January 1992 to December 2015 which analyze the predictive capability of search-based techniques for ascertaining four predominant software quality attributes, i.e., effort, defect proneness, maintainability and change proneness. The review analyses the effective use and application of search-based techniques by evaluating appropriate specifications of fitness functions, parameter settings, validation methods, accounting for their stochastic natures and the evaluation of developmental models with the use of well-known statistical tests. Furthermore, we compare the effectiveness of different models, developed using the various search-based techniques amongst themselves, and also with the prevalent machine learning techniques used in literature. Although there are very few studies which use search-based techniques for predicting maintainability and change proneness, we found that the results of the application of search-based techniques for effort estimation and defect prediction are encouraging. Hence, this comprehensive study and the associated results will provide guidelines to practitioners and researchers and will enable them to make proper choices for applying the search-based techniques to their specific situations.

      PubDate: 2016-10-29T13:13:26Z
      DOI: 10.1016/j.swevo.2016.10.002
  • An efficient gbest-guided Cuckoo Search algorithm for higher order two
           channel filter bank design
    • Authors: Supriya Dhabal; Palaniandavar Venkateswaran
      Abstract: Publication date: Available online 20 October 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Supriya Dhabal, Palaniandavar Venkateswaran
      This paper proposes a new algorithm based on Gbest-guided Cuckoo Search (GCS) algorithm for the design of higher order Quadrature Mirror Filter (QMF) bank. Although the optimization of lower order filters can be performed easily by applying existing meta-heuristic optimization techniques like Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) etc., these methods are unsuccessful in searching higher order filter coefficients due to multimodality and nonlinear problem space; leads to some undesirable behaviors in filter responses like ripples in transition band, lower stop-band attenuation etc.. Comparison with other available results in the literature indicate that the proposed method exhibits an 69.02% increase in stop-band attenuation and 99.71% reduction in Perfect Reconstruction Error (PRE) of higher order filter bank. Besides, the percentage improvements in Fitness Function Evaluations (FFEs) of GCS based 55th order QMF bank design with respect to PSO, ABC and CSA are 81%, 82% and 59% respectively, and execution time is improved by 73%, 72% and 42% respectively. The simulation results also reveal that the proposed approach exhibits lowest mean and variance in different assessment parameters of filter bank and it does not require tuning of algorithmic parameters whereas in standard CSA replacement factor need to be adjusted. Further, the proposed algorithm is tested on six standard benchmark problems and complex benchmark functions from the CEC 2013 where it demonstrated significant performance improvements than other existing methods.

      PubDate: 2016-10-29T13:13:26Z
      DOI: 10.1016/j.swevo.2016.10.003
  • Quasi-oppositional symbiotic organism search algorithm applied to load
           frequency control
    • Authors: Dipayan Guha; Provas Roy; Subrata Banerjee
      Abstract: Publication date: Available online 13 October 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Dipayan Guha, Provas Roy, Subrata Banerjee
      The present work approaches a relatively new optimization scheme called “quasi-oppositional symbiotic organism search (QOSOS) algorithm”, for the first time, to find an optimal and effective solution for load frequency control (LFC) problem of the power system. The symbiotic organism search (SOS) algorithm works on the effect of symbiotic interaction strategies adopted by an organism to survive and propagate in the ecosystem. To avoid the suboptimal solution and to accelerate the convergence speed, the theory of quasi-oppositional based learning (Q-OBL) is integrated with original SOS and used to solve the LFC problem. To demonstrate the effectiveness of QOSOS algorithm, two-area interconnected power system with nonlinearity effect of governor dead band and generation rate constraint is considered at the first instant, followed by the four-area power system showing the consequence of load perturbation. The structural simplicity, robust performance and acceptability of well-popular proportional-integral-derivative (PID) controller enforce to implement it as a secondary controller for the present analysis. The success of QOSOS algorithm is established by comparing the dynamic performances of concerned power system with those obtained by some recently published algorithms available in the literature. Furthermore, the robustness and sensitivity are analyzed for the concerned power system to judge the efficacy of the proposed QOSOS approach.

      PubDate: 2016-10-15T09:55:28Z
      DOI: 10.1016/j.swevo.2016.10.001
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: October 2016
      Source:Swarm and Evolutionary Computation, Volume 30

      PubDate: 2016-09-25T09:40:37Z
  • A New Multi-objective Evolutionary Framework for Community Mining in
           Dynamic Social Networks
    • Authors: Bara'a A. Attea; Haidar S. Khoder
      Abstract: Publication date: Available online 20 September 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Bara'a A. Attea, Haidar S. Khoder
      Evolutionary clustering – clustering in the presence of dynamic shifts of data's topological structure – has recently drawn remarkable attention wherein several algorithms are developed in the study of complex real networks. Despite the growing interests, all of the algorithms are designed based on seemingly the same principle. The primary principle in these evolutionary clustering frameworks is guided by decomposing the problem into two individual criteria, snapshot quality and temporal smoothness. Snapshot quality should properly cluster individuals of a network into interconnected communities. Temporal smoothness, on the other hand, should capture well the dynamic shift of the interconnected clusters from one time step to another. Thus, in the absence of any dynamic behavior, an evolutionary clustering model should be no more than a community detection one in a static network. Unfortunately, all of the developed algorithms are proposed based on discretion of the snapshot quality as a unified of both intra- and inter- connected community detection model while temporal cost as a community evolution detection model. The contribution of this paper starts by noting the limitation of the existing state-of-the-art algorithms. Despite their performance on dynamic complex networks, their formulations lack complete reflection of sufficient community detection model. Our framework, then, models the evolutionary clustering problem by hypothesizing that it should not depart too much from the community detection problem. To support this claim, an alternate decomposition perspective is proposed by projecting the problem, as a multi-objective optimization problem, in the light of snapshot and temporal of both intra- and inter-community scores. Two snapshot qualities are proposed to individually emphasize the role of intra- and inter- community scores, while temporal cost is proposed to cross-fertilize inter- community score. By applying one of the prominent multi-objective evolutionary algorithms (MOEAs) to solve the proposed multi-objective evolutionary clustering framework and testing it on several synthetic and real-world dynamic networks, we demonstrate the ability of the proposed model to address the problem more accurately than the existing state-of-the-art formulations.

      PubDate: 2016-09-20T09:18:08Z
      DOI: 10.1016/j.swevo.2016.09.001
  • Screening dense and noisy DOX-datasets with NN-blending and “dizzy”
           swarm intelligence: Profiling a water quality process
    • Authors: George J. Besseris
      Abstract: Publication date: Available online 24 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): George J. Besseris
      A novel nature-inspired method is presented in this work for resolving product/process development or improvement with design of experiments (DOX). The technique is suitable for difficult Taguchi-type multifactorial screening and optimization studies that need to simultaneously contain the double hassle of controllable and uncontrollable noise intrusions. The three-part sequential processing routine requires: 1) a regressive data-compression preprocessing, 2) a smart-sample generation using general-regression neural networks (GRNN), and 3) a screening power prediction using ‘reverse’ swarm intelligence (SI). The approach is primed to confront potential non-linearity and data messiness in the examined effects. The Taguchi-type orthogonal-array (OA) sampler is tuned for retrieving information in controllable (outer OA) and uncontrollable (inner OA) noises. The OA-saturation condition is elicited for maximum data exploitation. GRNN-fuzzification consolidates into a single contribution the uncertainty from all possible sources. The resulting ‘smart’ sample is defuzzified by a robust-and-agile data reduction. Screening-solution meta-power is controlled with a new SI-variant. The independent swarm groups, as many as the studied effects, are tracked toward preassigned targets, i.e. their ability to return to their host beehives. The technique is illustrated on a complex purification process where published multifactorial data had been collected for a critical wastewater paradigm and thus may be used to test the benchmark solution. However, environmental water-qualimetrics are profoundly dominated by messy data as justified in this work. We elucidate on several issues that regular Taguchi methods may be benefited by the proposed GRNN/SI processing while emphasizing the consequence of overlooking the underlying assumptions that govern standard comparison models. The new swarm itelligence method offered a practical way to estimate a first-time “soft” power measure for the inner/outer OA optimization case that was impossible with ordinary statistical multi-factorial treatments. Key performance advantages in efficiency, robustness and convenience are highlighted against alternative approaches.

      PubDate: 2016-08-27T01:40:21Z
      DOI: 10.1016/j.swevo.2016.08.003
  • BFOA-scaled fractional order fuzzy PID controller applied to AGC of
           multi-area multi-source electric power generating systems
    • Authors: Yogendra Arya; Narendra Kumar
      Abstract: Publication date: Available online 12 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Yogendra Arya, Narendra Kumar
      In the fast developing world of today, automatic generation control (AGC) plays an incredibly significant role in offering inevident demand of good quality power supply in power system. To deliver a quality power, AGC system requires an efficient and intelligent control algorithm. Hence, in this paper, a novel fractional order fuzzy proportional-integral-derivative (FOFPID) controller is proposed for AGC of electric power system. The proposed controller is tested for the first time on three structures of multi-area multi-source AGC system. The gains and fractional order parameters such as order of integrator (λ) and differentiator (µ and γ) of FOFPID controller are optimized using bacterial foraging optimization algorithm (BFOA). Initially, the proposed controller is implemented on a traditional two-area multi-source hydrothermal power system and its effectiveness is established by comparing the results with FOPID, fuzzy PID (FPID) and PI/PID controller based on recently published optimization techniques like hybrid firefly algorithm-pattern search (hFA-PS) and grey wolf optimization (GWO) algorithm. The approach is further extended to restructured multi-source hydrothermal and thermal gas systems. It is observed that the dynamic performance of the proposed BFOA optimized FOFPID controller is superior to BFOA optimized FPID/FOPID/PID and differential evolution/genetic algorithm optimized PID controllers. It is also detected that the dynamic responses obtained under different power transactions with/without appropriate generation rate constraint, time delay and governor dead-zone effectively satisfy the AGC requirement in deregulated environment. Moreover, robustness of the proposed approach is verified against wide variations in the nominal initial loading, system parameters, distribution company participation matrix structure and size and position of uncontracted power demand.

      PubDate: 2016-08-16T23:55:41Z
      DOI: 10.1016/j.swevo.2016.08.002
  • Integrated frequency and power control of an isolated hybrid power system
           considering scaling factor based fuzzy classical controller
    • Authors: Somnath. Ganguly; Tarkeshwar Mahto; V Mukherjee
      Abstract: Publication date: Available online 9 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Somnath. Ganguly, Tarkeshwar Mahto, V Mukherjee
      This paper describes an application of quasi-oppositional harmony search (QOHS) algorithm to design the scaling factor (SF) based fuzzy classical controller (such as PI/PD/PID) for frequency and power control of an isolated hybrid power system (IHPS). The considered IHPS model is comprised of a wind turbine generator, a diesel engine generator and an energy storage device (such as superconducting magnetic energy storage (SMES), in this case). Traditionally, SF, membership functions and control rules are obtained in fuzzy logic controllers (FLCs) by trial and error method or are obtained based on the experiences of the designers or are optimized by some traditional optimization techniques with some extra computational cost. To overcome all these problems of FLCs, classical controllers have been integrated in this paper with the FLC. QOHS algorithm is applied to simultaneously tune the SFs (the only tunable parameter of FLC), the gains of the classical controllers and the tunable parameters of the SMES device to minimize frequency and power deviations of the studied IHPS system against various load and wind change. Different considered controller configurations of the IHPS are SF based FLC (termed as Fuzzy-only), SF based FLC with proportional-integral (PI) (named as Fuzzy-PI) controller, SF based FLC with proportional-derivative (PD) (abbreviated as Fuzzy-PD) controller and SF based FLC with proportional-integral-derivative (PID) (designated as Fuzzy-PID) controller. Simulation results, explicitly, show that the performance of the Fuzzy-PID controller based IHPS is superior to Fuzzy-only, Fuzzy-PI and Fuzzy-PD controller based IHPS configuration in terms of overshoot, settling time and the proposed Fuzzy-PID controller is robust against various wide range of load changes.

      PubDate: 2016-08-11T23:37:02Z
      DOI: 10.1016/j.swevo.2016.08.001
  • Gravitational Swarm Optimizer for Global Optimization
    • Authors: Anupam Yadav; Kusum Deep; Joong Hoon Kim; Atulya K. Nagar
      Abstract: Publication date: Available online 2 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anupam Yadav, Kusum Deep, Joong Hoon Kim, Atulya K Nagar
      In this article, a new meta-heuristic method is proposed by combining particle swarm optimization (PSO) and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the article. The efficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs efficiently with good results as a constrained optimizer.
      Graphical abstract image Highlights

      PubDate: 2016-08-06T22:52:04Z
      DOI: 10.1016/j.swevo.2016.07.003
  • Experimentation investigation of abrasive water jet machining parameters
           using Taguchi and Evolutionary optimization techniques
    • Authors: Rajkamal Shukla; Dinesh Singh
      Abstract: Publication date: Available online 26 July 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Rajkamal Shukla, Dinesh Singh
      In the last decade, numerous new materials are rapidly emerging and developed; it creates considerable interest in the researcher to search out the optimum combination of machining parameters during machining of these materials using advanced machining processes (AMP). In this work, an experimental investigation is carried out on abrasive water jet machining (AWJM) process for the machining of material AA631-T6 using the Taguchi methodology. Parameters such as transverse speed, standoff distance and mass flow rate are considered to obtain the influence of these parameters on kerf top width and taper angle. Regression models have been developed to correlate the data generated using experimental results. Seven advanced optimization techniques, i.e., particle swarm optimization, firefly algorithm, artificial bee colony, simulated annealing, black hole, biogeography based and non-dominated sorting genetic algorithm are attempted for the considered AWJM process. The effectiveness of these algorithms is compared and found that bio-geography algorithm is performing better compared to other algorithms. Furthermore, a non-dominated set of solution is obtained to have diversity in the solutions for the AWJM process. The result obtained using the Taguchi method and optimization techniques are confirmed using confirmation experiments. Confirmatory experiments show that both the optimization techniques and Taguchi method are the effective tools in optimizing the process parameters of the AWJM process.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.07.002
  • Computing with the Collective Intelligence of Honey Bees – A Survey
    • Authors: Anguluri Rajasekhar; Nandar Lynn; Swagatam Das; P.N. Suganthan
      Abstract: Publication date: Available online 15 July 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Anguluri Rajasekhar, Nandar Lynn, Swagatam Das, P.N. Suganthan
      Over past few decades, families of algorithms based on the intelligent group behaviors of social creatures like ants, birds, fishes, and bacteria have been extensively studied and applied for computer-aided optimization. Recently there has been a surge of interest in developing algorithms for search, optimization, and communication by simulating different aspects of the social life of a very well-known creature: the honey bee. Several articles reporting the success of the heuristics based on swarming, mating, and foraging behaviors of the honey bees are being published on a regular basis. In this paper we provide a brief but comprehensive survey of the entire horizon of research so far undertaken on the algorithms inspired by the honey bees. Starting with the biological perspectives and motivations, we outline the major bees-inspired algorithms, their prospects in the respective problem domains and their similarities and dissimilarities with the other swarm intelligence algorithms. We also provide an account of the engineering applications of these algorithms. Finally we identify some open research issues and promising application areas for the bees-inspired computing techniques.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.001
  • Swarm intelligence inspired classifiers for facial recognition
    • Authors: Salima Nebti; Abdallah Boukerram
      Abstract: Publication date: Available online 9 July 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Salima Nebti, Abdallah Boukerram
      Facial recognition is a challenging issue in pattern recognition arising from the need for high security systems capable of overcoming the variability of the acquisition environment such as illumination, pose or facial expression. A broad range of recognition methods have been suggested, yet most are still unable to yield optimal accuracy. More recently, new methods based on swarm intelligence or classifiers combination have been devised in the field of facial recognition. Swarm intelligence based methods aim to achieve effective recognition accuracy by exploiting their global optimization capability. The combination of classifiers is a new trend allowing cooperation between multiple classifiers. In this work, two classifiers inspired from swarm intelligence are proposed: a bees algorithm based classifier and a decision tree based binary particle swarm optimization classifier. The two are then combined with a decision tree based fuzzy support vector machine by using the majority vote as an attempt to compensate for the weakness of single classifiers. Moreover, the impact of different characteristic features and space reduction methods has been examined namely, the Gabor magnitude and the Gabor phase congruency features in combination with PCA, LDA or KFA reduction space methods. The experiments were conducted on four popular databases: ORL, YALE, FERET and UMIST. The results revealed that the proposed swarm intelligence based classifiers are very effective compared to similar classifiers in terms of recognition accuracy.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.07.001
  • Swarm and evolutionary computing algorithms for system identification and
           filter design: A comprehensive review
    • Authors: Akhilesh Gotmare; Sankha Subhra Bhattacharjee; Rohan Patidar; Nithin V. George
      Abstract: Publication date: Available online 23 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar, Nithin V. George
      An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.007
  • A comprehensive survey of traditional, merge-split and evolutionary
           approaches proposed for determination of cluster number
    • Authors: Emrah Hancer; Dervis Karaboga
      Abstract: Publication date: Available online 23 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Emrah Hancer, Dervis Karaboga
      Today's data mostly does not include the knowledge of cluster number. Therefore, it is not possible to use conventional clustering approaches to partition today's data, i.e., it is necessary to use the approaches that automatically determine the cluster number or cluster structure. Although there has been a considerable attempt to analyze and categorize clustering algorithms, it is difficult to find a survey paper in the literature that has thoroughly focused on the determination of cluster number. This significant issue motivates us to introduce concepts and review methods related to automatic cluster evolution from a theoretical perspective in this study.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.004
  • Effective heuristics for ant colony optimization to handle large-scale
    • Authors: Hassan Ismkhan
      Abstract: Publication date: Available online 22 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Hassan Ismkhan
      Although ant colony optimization (ACO) has successfully been applied to a wide range of optimization problems, its high time- and space-complexity prevent it to be applied to the large-scale instances. Furthermore, local search, used in ACO to increase its performance, is applied without using heuristic information stored in pheromone values. To overcome these problems, this paper proposes new strategies including effective representation and heuristics, which speed up ACO and enable it to be applied to large-scale instances. Results show that in performed experiments, proposed ACO has better performance than other versions in terms of accuracy and speed.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.006
  • Genetic algorithms to balanced tree structures in graphs
    • Authors: Riham Moharam; Ehab Morsy
      Abstract: Publication date: Available online 22 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Riham Moharam, Ehab Morsy
      Given an edge-weighted graph G = ( V , E ) with vertex set V and edge set E, we study in this paper the following related balanced tree structure problems in G. The first problem, called Constrained Minimum Spanning Tree Problem (CMST), asks for a rooted tree T in G that minimizes the total weight of T such that the distance between the root and any vertex v in T is at most a given constant C times the shortest distance between the two vertices in G. The Constrained Shortest Path Tree Problem (CSPT) requires a rooted tree T in G that minimizes the maximum distance between the root and all vertices in V such that the total weight of T is at most a given constant C times the minimum tree weight in G. The third problem, called Minimum Maximum Stretch Spanning Tree (MMST), looks for a tree T in G that minimize the maximum distance between all pairs of vertices in V. It is easy to conclude from the literatures that the above problems are NP-hard. We present efficient genetic algorithms that return (as shown by our experimental results) high quality solutions for these problems.

      PubDate: 2016-08-01T22:20:16Z
      DOI: 10.1016/j.swevo.2016.06.005
  • A quantum-inspired genetic algorithm for solving the antenna positioning
    • Authors: Zakaria Abd El Moiz Dahi; Chaker Mezioud; Amer Draa
      Abstract: Publication date: Available online 16 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Zakaria Abd El Moiz Dahi, Chaker Mezioud, Amer Draa
      Cellular phone networks are one of today's most popular means of communication. The big popularity and accessibility of the services proposed by these networks have made the mobile industry a field with high standard and competition where service quality is key. Actually, such a quality is strongly bound to the design quality of the networks themselves, where optimisation issues exist at each step. Thus, any process that cannot cope with these problems may alter the design phase and ultimately the service provided. The Antenna Positioning Problem (APP) is one of the most determinant optimisation issues that engineers face during network life cycle. This paper proposes a new variant of the Quantum-Inspired Genetic Algorithm (QIGA) based on a novel quantum gate for solving the APP. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. Several statistical analysis tests have been carried as well. State-of-the-art algorithms designed to solve the APP, the Population-Based Incremental Learning (PBIL) and Genetic Algorithm (GA), are taken as a comparison basis. Performance evaluation of the proposed approach proves that it is efficient, robust and scalable; it could outperform both PBIL and GA in many benchmark instances.

      PubDate: 2016-06-18T18:03:36Z
      DOI: 10.1016/j.swevo.2016.06.003
  • A competitive memetic algorithm for multi-objective distributed
           permutation flow shop scheduling problem
    • Authors: Jin Deng; Ling Wang
      Abstract: Publication date: Available online 8 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Jin Deng, Ling Wang
      In this paper, a competitive memetic algorithm (CMA) is proposed to solve the multi-objective distributed permutation flow-shop scheduling problem (MODPFSP) with the makespan and total tardiness criteria. Two populations corresponding to two different objectives are employed in the CMA. Some objective-specific operators are designed for each population, and a special interaction mechanism between two populations is designed. Moreover, a competition mechanism is proposed to adaptively adjust the selection rates of the operators, and some knowledge-based local search operators are developed to enhance the exploitation ability of the CMA. In addition, the influence of the parameters on the performance of the CMA is investigated by using the Taguchi method of design-of-experiment. Finally, extensive computational tests and comparisons are carried out to demonstrate the effectiveness of the CMA in solving the MODPFSP.

      PubDate: 2016-06-13T02:27:18Z
      DOI: 10.1016/j.swevo.2016.06.002
  • Review of differential evolution population size
    • Authors: Adam P. Piotrowski
      Abstract: Publication date: Available online 4 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Adam P. Piotrowski
      Population size of Differential Evolution (DE) algorithms is often specified by user and remains fixed during run. During the first decade since the introduction of DE the opinion that its population size should be related to the problem dimensionality prevailed, later the approaches to DE population size setting diversified. In large number of recently introduced DE algorithms the population size is considered to be problem-independent and often fixed to 100 or 50 individuals, but alongside a number of DE variants with flexible population size have been proposed. The present paper briefly reviews the opinions regarding DE population size setting and verifies the impact of the population size on the performance of DE algorithms. Ten DE algorithms with fixed population size, each with at least five different population size settings, and four DE algorithms with flexible population size are tested on CEC2005 benchmarks and CEC2011 real-world problems. It is found that the inappropriate choice of the population size may severely hamper the performance of each DE algorithm. Although the best choice of the population size depends on the specific algorithm and problem to be solved, some rough guidelines may be sketched. For low-dimensional problems (with dimensionality below 30) the population size equal to 100 individuals is suggested; population sizes smaller than 50 are rarely advised. For higher-dimensional artificial problems the population size should often depend on the problem dimensionality d and be set to 3d–5d. Unfortunately, setting proper population size for higher-dimensional real-world problems (d > 40) turns out too problem and algorithm-dependent to give any general guide; 200 individuals may be a first guess, but many DE approaches would need a much different choice, ranging from 50 to 10d. However, quite clear relation between the population size and the convergence speed has been found, showing that the fewer function calls are available, the lower population sizes perform better. Based on the extensive experimental results the use of adaptive population size is highly recommended, especially for higher-dimensional and real-world problems. However, which specific algorithms with population size adaptation perform better depends on the number of function calls allowed.

      PubDate: 2016-06-13T02:27:18Z
      DOI: 10.1016/j.swevo.2016.05.003
  • The influence of population size in geometric semantic GP
    • Authors: Mauro Castelli; Luca Manzoni; Sara Silva; Leonardo Vanneschi; Aleš Popovič
      Abstract: Publication date: Available online 3 June 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Mauro Castelli, Luca Manzoni, Sara Silva, Leonardo Vanneschi, Aleš Popovič
      In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances.

      PubDate: 2016-06-13T02:27:18Z
      DOI: 10.1016/j.swevo.2016.05.004
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