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Journal Cover Swarm and Evolutionary Computation
  [SJR: 5.631]   [H-I: 13]   [1 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [2969 journals]
  • Screening dense and noisy DOX-datasets with NN-blending and “dizzy”
           swarm intelligence: Profiling a water quality process
    • 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
       
  • Evolutionary and GPU computing for topology optimization of structures
    • Abstract: Publication date: Available online 24 August 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Laxman Ram, Deepak Sharma
      Although Structural topology Optimization, as a discrete optimization problem, has been successfully solved several times in the literature using evolutionary algorithms (EAs), the two key difficulties lie in generating geometrically feasible structures and handling a high computation time. These two challenges are addressed in this paper by adopting triangular representation for two-dimensional continuum structures, related crossover and mutation operators, and by performing computations in parallel on the graphics processing unit (GPU). Two case studies are solved on the GPU that show 5× of speedup over CPU implementation. The parametric study on the population size of EA shows that the approximate Pareto-optimal solutions can be evolved using a small population with the proposed EA operators.


      PubDate: 2016-08-27T01:40:21Z
       
  • BFOA-scaled fractional order fuzzy PID controller applied to AGC of
           multi-area multi-source electric power generating systems
    • 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
       
  • Integrated frequency and power control of an isolated hybrid power system
           considering scaling factor based fuzzy classical controller
    • 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
       
  • Gravitational Swarm Optimizer for Global Optimization
    • 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
       
  • Effective heuristics for ant colony optimization to handle large-scale
           problems
    • 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
       
  • Genetic algorithms to balanced tree structures in graphs
    • 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
       
  • A comprehensive survey of traditional, merge-split and evolutionary
           approaches proposed for determination of cluster number
    • 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
       
  • Swarm and evolutionary computing algorithms for system identification and
           filter design: A comprehensive review
    • 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
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: August 2016
      Source:Swarm and Evolutionary Computation, Volume 29




      PubDate: 2016-08-01T22:20:16Z
       
  • Experimentation investigation of abrasive water jet machining parameters
           using Taguchi and Evolutionary optimization techniques
    • 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
       
  • Swarm intelligence inspired classifiers for facial recognition
    • 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
       
  • Computing with the Collective Intelligence of Honey Bees – A Survey
    • 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
       
  • A quantum-inspired genetic algorithm for solving the antenna positioning
           problem
    • 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
       
  • Maintaining regularity and generalization in data using the minimum
           description length principle and genetic algorithm: Case of grammatical
           inference
    • Abstract: Publication date: Available online 17 May 2016
      Source:Swarm and Evolutionary Computation
      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: 2016-06-13T02:27:18Z
       
  • The influence of population size in geometric semantic GP
    • 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
       
  • PEAR: a massively parallel evolutionary computational approach for
           political redistricting optimization and analysis
    • Abstract: Publication date: Available online 26 May 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Yan Y. Liu, Wendy K. Tam Cho, Shaowen Wang
      Political redistricting, a well-known problem in political science and geographic information science, can be formulated as a combinatorial optimization problem, with objectives and constraints defined to meet legal requirements. The formulated optimization problem is NP-hard. We develop a scalable evolutionary computational approach utilizing massively parallel high performance computing for political redistricting optimization and analysis at fine levels of granularity. Our computational approach is based in strong substantive knowledge and deep adherence to Supreme Court mandates. Since the spatial configuration plays a critical role in the effectiveness and numerical efficiency of redistricting algorithms, we have designed spatial evolutionary algorithm (EA) operators that incorporate spatial characteristics and effectively search the solution space. Our parallelization of the algorithm further harnesses massive parallel computing power provided by supercomputers via the coupling of EA search processes and a highly scalable message passing model that maximizes the overlapping of computing and communication at runtime. Experimental results demonstrate desirable effectiveness and scalability of our approach (up to 131K processors) for solving large redistricting problems, which enables substantive research into the relationship between democratic ideals and phenomena such as partisan gerrymandering.


      PubDate: 2016-06-13T02:27:18Z
       
  • Review of differential evolution population size
    • 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
       
  • A competitive memetic algorithm for multi-objective distributed
           permutation flow shop scheduling problem
    • 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
       
  • Node-depth Phylogenetic-Based Encoding, a Spanning-Tree Representation for
           Evolutionary Algorithms. Part I: Proposal and Properties Analysis
    • Abstract: Publication date: Available online 13 May 2016
      Source:Swarm and Evolutionary Computation
      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: 2016-05-14T00:11:44Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2016
      Source:Swarm and Evolutionary Computation, Volume 28




      PubDate: 2016-05-07T23:42:42Z
       
  • Using autonomous search for solving constraint satisfaction problems via
           new modern approaches
    • Abstract: Publication date: Available online 21 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Ricardo Soto, Broderick Crawford, Rodrigo Olivares, Cristian Galleguillos, Carlos Castro, Franklin Johnson, Fernando Paredes, Enrique Norero
      Constraint Programming is a powerful paradigm which allows the resolution of many complex problems, such as scheduling, planning, and configuration. These problems are defined by a set of variables and a set of constraints. Each variable has non-empty domain of possible value and each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Autonomous search is a particular case of adaptive systems that aims at improving its solving performance by adapting itself to the problem at hand without manual configuration of an expert user. The goal is to improve their solving performance by modifying and adjusting themselves, either by self-adaptation or by supervised adaptation. This approach has been effectively applied to different optimization and satisfaction techniques such as constraint programming, metaheuristics, and SAT. In this paper, we present a new Autonomous Search approach for constraint programming based on four modern bio-inspired metaheuristics. The goal of those metaheuristics is to optimize the self-tuning phase of the constraint programming search process. We illustrate promising results, where the proposed approach is able to efficiently solve several well-known constraint satisfaction problems.


      PubDate: 2016-04-26T22:54:15Z
       
  • An object tracking method using modified galaxy-based search algorithm
    • 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
       
  • COOA: Competitive Optimization Algorithm
    • Abstract: Publication date: Available online 18 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Yousef Sharafi, Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab
      This paper presents a novel optimization algorithm based on competitive behavior of various creatures such as birds, cats, bees and ants to survive in nature. In the proposed method, a competition is designed among all aforementioned creatures according to their performances. Every optimization algorithm can be appropriate for some objective functions and may not be appropriate for another. Due to the interaction between different optimization algorithms proposed in this paper, the algorithms acting based on the behavior of these creatures can compete each other for the best. The rules of competition between the optimization methods are based on imperialist competitive algorithm. Imperialist competitive algorithm decides which of the algorithms can survive and which of them must be extinct. In order to have a comparison to well-known heuristic global optimization methods, some simulations are carried out on some benchmark test functions with different and high dimensions. The obtained results shows that the proposed competition based optimization algorithm is an efficient method in finding the solution of optimization problems.


      PubDate: 2016-04-21T14:34:39Z
       
  • Bio-inspired search algorithms for unstructured P2P overlay networks
    • Abstract: Publication date: Available online 4 April 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Vesna Šešum-Čavić, Eva Kühn, Daniel Kanev
      Efficient location and manipulation of complex and often incomplete data is a difficult, challenging task in nowadays extremely complex IT systems and on the Internet, overwhelmed with a huge amount of information. The problem itself is present in numerous different practical use-cases (e.g., in P2P streaming applications that rapidly gain more attention) and refers to the selection of the proper, efficient search algorithm. Research and commercial efforts resulted in a prolific offer of different algorithms that try to address this problem in the best possible way. Due to the huge complexity, intelligent algorithms are the most promising ones. However, everyday changing conditions impose finding even more advantageous approaches that will better cope with the problem, or at least address some “corner cases” better, than previously realized ones. In this paper, we propose a self-organizing approach inspired by bio-intelligence of slime molds that possesses distributive and autonomous properties with the goal to achieve a good query capability. A slime mold mechanism is adapted for search in an unstructured P2P system, and compared with Antnet and Gnutella search mechanisms. The benchmarks cover parameter sensitivity analysis, and comparative analysis. To validate the obtained results, a statistical analysis is performed. The obtained results show good scalability of slime mold algorithm and point to the selected “corner” cases where the slime mold algorithm has a total good performance (measured by different metrics).


      PubDate: 2016-04-06T15:57:56Z
       
  • Hybrid self-adaptive Cuckoo search for global optimization
    • Abstract: Publication date: Available online 29 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Uroš Mlakar, Iztok Fister, Iztok Fister
      Adaptation and hybridization typically improve the performances of original algorithm. This paper proposes a novel hybrid self-adaptive cuckoo search algorithm, which extends the original cuckoo search by adding three features, i.e., a balancing the exploration search strategies within the cuckoo search algorithm, a self-adaptation of cuckoo search control parameters and a linear population reduction. The algorithm was tested on 30 benchmark functions from the CEC-2014 test suite, giving promising results comparable to the algorithms, like the original differential evolution (DE) and original cuckoo search (CS), some powerful variants of modified cuckoo search (i.e., MOCS, CS-VSF) and self-adaptive differential evolution (i.e., jDE, SaDE), while overcoming the results of a winner of the CEC-2014 competition L-Shade remains a great challenge for the future.


      PubDate: 2016-04-01T15:30:25Z
       
  • Quantum Inspired Social Evolution (QSE) Algorithm for 0-1 Knapsack problem
    • Abstract: Publication date: Available online 11 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): R.S. Pavithr, Gursaran
      Social Evolution (SE) algorithm [10] is inspired by human interactions and their bias. Generally, human bias influences with whom individuals interact and how they interact. The individual bias may also influence the outcome of interactions to be decisive or indecisive. When the interactions are decisive, the individuals may completely adopt the change. When the interactions are indecisive, individual may consult for a second opinion to further evaluate the indecisive interaction before adopting the change to emerge and evolve [10]. In the last decade, with the integration of emerging quantum computing with the traditional evolutional algorithms, quantum inspired evolutionary algorithm is evolved [2]. Inspired by Q-bit representation and parallelism and the success of the quantum inspired evolutionary algorithms, in this paper, a quantum inspired Social Evolution algorithm (QSE) is proposed by hybridizing Social evolution algorithm with the emerging quantum inspired evolutionary algorithm. The proposed QSE algorithm is studied on a well known 0-1 knapsack problem and the performance of the algorithm is compared with various evolutionary, swarm and quantum inspired evolutionary algorithm variants. The results indicate that, the performance of QSE algorithm is better than or comparable with the different evolutionary algorithmic variants tested with. An experimental study is also performed to investigate the impact and importance of human bias in selection of individuals for interactions, the rate of individuals seeking for second opinion and the influence of selective learning on the overall performance of Quantum inspired Social Evolution algorithm (QSE).


      PubDate: 2016-03-12T16:17:45Z
       
  • Multi-dimensional signaling method for population-based metaheuristics:
           Solving the large-scale scheduling problem in smart grids
    • Abstract: Publication date: Available online 8 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): João Soares, M. Ali Fotouhi, Marco Silva, Zita Vale
      The dawn of smart grid is posing new challenges to grid operation. The introduction of Distributed Energy Resources (DER) requires tough planning and advanced tools to efficiently manage the system at reasonable costs. Virtual Power Players (VPP) are used as means of aggregating generation and demand, which enable smaller producers using different generation technologies to be more competitive. This paper discusses the problem of the centralized Energy Resource Management (ERM), including several types of resources, such as Demand Response (DR), Electric Vehicles (EV) and Energy Storage Systems (ESS) from the VPP׳s perspective to maximize profits. The complete formulation of this problem, which includes the network constraints, is represented with a complex large-scale mixed integer nonlinear problem. This paper focuses on deterministic and metaheuristics methods and proposes a new multi-dimensional signaling approach for population-based random search techniques. The new approach is tested with two networks with high penetration of DERs. The results show outstanding performance with the proposed multi-dimensional signaling and confirm that standard metaheuristics are prone to fail in solving these kind of problems.


      PubDate: 2016-03-12T16:17:45Z
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: April 2016
      Source:Swarm and Evolutionary Computation, Volume 27




      PubDate: 2016-03-12T16:17:45Z
       
  • A New Cuckoo Search Algorithm for 2-Machine Robotic Cell Scheduling
           Problem with Sequence-Dependent Setup Times
    • Abstract: Publication date: Available online 11 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Arindam Majumder, Dipak Laha
      The paper addresses the problem of 2-machine robotic cell scheduling of one-unit cycle with sequence-dependent setup times and different loading/unloading times of the parts. As an alternative metaheuristic algorithm, the cuckoo search algorithm has recently attracted growing interests of researchers. It has the capability to search globally as well as locally to converge to the global optimality by exploring the search space more efficiently for to its global random walk governed by Levy flights, rather than standard isotropic random walk. In this study, a discrete cuckoo search algorithm is proposed to determine the sequence of robot moves along with the sequence of parts so that the cycle time is minimized. In the proposed algorithm, the fractional scaling factor based procedure is presented to determine the step length of Levy flights distribution in discrete from and then, using this step length, two neighborhood search techniques, interchange and cyclical shift methods are applied to the current solution to obtain improved solution. A response surface methodology based on desirability function is used to enhance the convergence speed of the proposed algorithm. Also, a design of experiment is employed to tune the operating parameters of the algorithm. Finally, empirical results with a large number of randomly generated problem instances involving large part sizes varying from 200 to 500 under different operating conditions are compared with two well-known algorithms in the literature and demonstrate the effectiveness of the proposed algorithm.


      PubDate: 2016-03-12T16:17:45Z
       
  • A Hierarchical Heterogeneous Ant Colony Optimization based approach for
           efficient Action Rule mining
    • Abstract: Publication date: Available online 5 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): N.K. Sreeja, A. Sankar
      Most data mining algorithms aim at discovering customer models and classification of customer profiles. Application of these data mining techniques to industrial problems such as customer relationship management helps in classification of customers with respect to their status. The mined information does not suggest any action that would result in reclassification of customer profile. Such actions would be useful to maximize the objective function, for instance, the net profit or minimizing the cost. These actions provide hints to a business user regarding the attributes that have to be changed to reclassify the customers from an undesirable class (Eg. disloyal) to the desired class (Eg. loyal). This paper proposes a novel algorithm called Hierarchical Heterogeneous Ant Colony Optimization based Action Rule Mining (HHACOARM) algorithm to generate action rules. The algorithm has been developed considering the resource constraints. The algorithm has ant agents at different levels in the hierarchy to identify the flexible attributes whose values need to be changed to mine action rules. The advantage of HHACOARM algorithm is that it generates optimal number of minimal cost action rules. HHACOARM algorithm does not generate invalid rules. Also, the computational complexity of HHACOARM algorithm is less compared to the existing action rule mining methods.


      PubDate: 2016-03-07T16:06:16Z
       
  • Application of hybrid heuristic optimization algorithms for solving
           optimal regional dispatch of energy and reserve considering the social
           welfare of the participating markets
    • Abstract: Publication date: Available online 2 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Marlon S. Chamba, Osvaldo Añó, Rodolfo Reta
      Market integration allows increasing the social welfare of a given society. In most markets, integration also raises the social welfare of the participating markets (partakers). However, electricity markets have complexities such as transmission network congestion and requirements of power reserve that could lead to a decrease in the social welfare of some partakers. The social welfare reduction of partakers, if it occurs, would surely be a hindrance to the development of regional markets, since participants are usually national systems. This paper shows a new model for the regional dispatch of energy and reserve, and proposes as constraints that the social welfare of partakers does not decrease with respect to that obtained from the isolated optimal operation. These social welfare constraints are characterized by their stochastic nature and their dependence on the energy price of different operating states. The problem is solved by the combination of two optimization models (hybrid optimization): A linear model embedded within a meta-heuristic algorithm, which is known as the swarm version of the Means Variance Mapping Optimization (MVMOS). MVMOS allows incorporating the stochastic nature of social welfare constraints through a dynamic penalty scheme, which considers the fulfillment degree along with the dynamics of the search process.


      PubDate: 2016-03-07T16:06:16Z
       
  • Microarray medical data classification using kernel ridge regression and
           modified cat swarm optimization based gene selection system
    • Abstract: Publication date: Available online 2 March 2016
      Source:Swarm and Evolutionary Computation
      Author(s): P. Mohapatra, S. Chakravarty, P.K. Dash
      Microarray gene expression based medical data classification has remained as one of the most challenging research areas in the field of bioinformatics, machine learning and pattern classification. This paper proposes two variations of kernel ridge regression (KRR), namely wavelet kernel ridge regression (WKRR) and radial basis kernel ridge regression (RKRR) for classification of microarray medical datasets. Microarray medical datasets contain irrelevant and redundant genes which cause high number of gene expression i.e. dimensionality and small sample sizes. To overcome the curse of dimensionality of the microarray datasets, modified cat swarm optimization (MCSO), a naturally inspired evolutionary algorithm, is used to select the most relevant features from the datasets. The adequacies of the classifiers are demonstrated by employing four from each binary and multi-class microarray medical datasets. Breast cancer, prostate cancer, colon tumor, leukemia datasets belong to the former and leukemia1, leukemia2, SRBCT, brain tumor1 to the latter. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and the area under the receiver operating characteristic (ROC) curve are considered to examine the efficacy of the model. Other models like simple ridge regression (RR), online sequential ridge regression (OSRR), support vector machine radial basis function (SVMRBF), support vector machine polynomial (SVMPoly) and random forest are studied and analyzed for comparison. The experimental results demonstrate that KRR outperforms other models irrespective of the datasets and WKRR produces better results as compared to RKRR. Finally, when the results are compared on the basis of binary and multi-class datasets, it is found that binary class yields a little bit better result as compared to the multiclass irrespective of models.


      PubDate: 2016-03-07T16:06:16Z
       
  • Ageist Spider Monkey Optimization Algorithm
    • Abstract: Publication date: Available online 18 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Avinash Sharma, Akshay Sharma, BK Panigrahi, Deep Kiran, Rajesh Kumar
      Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems, have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient. A spider monkey group consists of members from every age group. The agility and swiftness of the spider monkeys differ on the basis of their age groups. This paper proposes a new variant of SMO algorithm termed as Ageist Spider Monkey Optimization (ASMO) algorithm which seems more practical in biological terms and works on the basis of age difference present in spider monkey population. Experiments on different benchmark functions with different parameters and settings have been carried out and the variant with the best suited settings is proposed. This variant of SMO has enhanced the performance of its original version. Also, ASMO has performed better in comparison to some of the recent advanced algorithms.


      PubDate: 2016-02-23T08:30:35Z
       
  • Directionally Driven Self-Regulating Particle Swarm Optimization algorithm
    • Abstract: Publication date: Available online 4 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): M.R. Tanweer, R. Auditya, S. Suresh, N. Sundararajan, N. Srikanth
      In this paper, an improved variant of the Self-Regulating Particle Swarm Optimization (SRPSO) algorithm is proposed that further enhances the performance of the basic SRPSO algorithm and is referred to as a Directionally Driven Self-Regulating Particle Swarm Optimization (DD-SRPSO) algorithm. In DD-SRPSO, we incorporate two new strategies, namely, a directional update strategy and a rotational invariant strategy. As in SRPSO, the best particle in DD-SRPSO uses the same self-regulated inertia weight update strategy. The poorly performing particles are grouped together to get directional updates from the group of elite particles. All the remaining particles are randomly selected to undergo either the SRPSO strategy of self-perception of the global search direction or the rotational invariant strategy to explore the rotation variance property of the search space. The performance of the DD-SRPSO algorithm is evaluated using the complex, shifted and rotated benchmark functions from CEC2013. These results are compared with seven current state-of-the-art PSO variants like Social Learning PSO (SLPSO), Comprehensive Learning PSO (CLPSO), SRPSO, etc. The results clearly indicate that the proposed learning strategies have significantly enhanced the performance of the basic SRPSO algorithm. The performance has also been compared with state-of-the-art evolutionary algorithms like Mean Variance Mapping Optimization (MVMO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) on the recently proposed numerically expensive optimzation CEC2015 benchmark functions whereby DD-SRPSO has provided competitive solutions. The results also indicate that the DD-SRPSO algorithm achieves a faster convergence and provides better solutions in a diverse set of problems with a 95% confidence level, thereby promising to be an effective optimization algorithm for real-world applications.


      PubDate: 2016-02-12T13:16:26Z
       
  • Recent advances in differential evolution – An updated survey
    • Abstract: Publication date: Available online 1 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Swagatam Das, Sankha Subhra Mullick, P.N. Suganthan
      Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.


      PubDate: 2016-02-12T13:16:26Z
       
  • Metaheuristics in structural optimization and discussions on harmony
           search algorithm
    • Abstract: Publication date: Available online 2 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): M.P. Saka, O. Hasançebi, Z.W. Geem
      Metaheuristic algorithms have provided efficient tools to engineering designers by which it became possible to determine the optimum solutions of engineering design optimization problems encountered in every day practice. Generally metaheuristics are based on metaphors that are taken from nature or some other processes. Because of their success of providing solutions to complex engineering design optimization problems the recent literature has flourished with a large number of new metaheuristics based on a variety of metaphors. Despite the fact that most of these techniques have numerically proven themselves as reliable and strong tools for solutions of design optimization problems in many different disciplines, some argue against these methods on account of not having mathematical background and making use of irrelevant and odd metaphors. However, so long as these efforts bring about computationally efficient and robust optimum structural tools for designers what type of metaphors they are based on becomes insignificant. After a brief historical review of structural optimization this article opens this issue up for discussion of the readers and attempts to answer some of the criticisms asserted in some recent publications related with the novelty of metaheuristics.


      PubDate: 2016-02-12T13:16:26Z
       
  • Prediction based mean-variance model for constrained portfolio assets
           selection using multiobjective evolutionary algorithms
    • Abstract: Publication date: Available online 1 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Sudhansu Kumar Mishra, Ganapati Panda, Babita Majhi
      In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns are predicted, using a low complexity heuristic functional link artificial neural network (HFLANN) model and the portfolio optimization task is carried out by using multi-objective evolutionary algorithms (MOEAs). In this paper, swarm intelligence based, multiobjective optimization algorithm, namely self-regulating multiobjective particle swarm optimization (SR-MOPSO) has also been proposed and employed efficiently to solve this important problem. The Pareto solutions obtained by applying two other competitive MOEAs and using the proposed PBMV models and Markowitz mean-variance model have been compared, considering six performance metrics and the Pareto fronts. Moreover, in the present study, the nonparametric statistical analysis using the Sign test and Wilcoxon rank test are also carried out, to compare the performance of the algorithms pair wise. It is observed that, the proposed PBMV model based approach provides better Pareto solutions, maintaining adequate diversity, and also quite comparable to the Markowitz model. From the simulation result, it is observed that the self regulating multiobjective particle swarm optimization (SR-MOPSO) algorithm based on PBMV model, provides the best Pareto solutions amongst those offered by other MOEAs.


      PubDate: 2016-02-12T13:16:26Z
       
  • A multilevel ACO approach for solving forest transportation planning
           problems with environmental constraints
    • Abstract: Publication date: Available online 3 February 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Pengpeng Lin, Marco A. Contreras, Ruxin Dai, Jun Zhang
      This paper presents a multilevel ant colony optimization (MLACO) approach to solve constrained forest transportation planning problems (CFTPPs). A graph coarsening technique is used to coarsen a network representing the problem into a set of increasingly coarser level problems. Then, a customized ant colony optimization (ACO) algorithm is designed to solve the CFTPP from coarser to finer level problems. The parameters of the ACO algorithm are automatically configured by evaluating a parameter combination domain through each level of the problem. The solution obtained by the ACO for the coarser level problems is projected into finer level problem components, which are used to help the ACO search for finer level solutions. The MLACO was tested on 20 CFTPPs and solutions were compared to those obtained from other approaches including a mixed integer programming (MIP) solver, a parameter iterative local search (ParamILS) method, and an exhaustive ACO parameter search method. Experimental results showed that the MLACO approach was able to match solution qualities and reduce computing time significantly compared to the tested approaches.


      PubDate: 2016-02-12T13:16:26Z
       
  • Solving Multi-objective Portfolio Optimization Problem Using Invasive Weed
           Optimization
    • Abstract: Publication date: Available online 15 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Amir Rezaei Pouya, Maghsud Solimanpur, Mustafa Jahangoshai Rezaee
      Portfolio optimization is one of the important issues for effective and economic investment. There is plenty of research in the literature addressing this issue. Most of these pieces of research attempt to make the Markowitz’s primary portfolio selection model more realistic or seek to solve the model for obtaining fairly optimum portfolios. In this paper, P E criterion and Experts’ Recommendations on Market Sectors have been added to the primary Markowitz mean-variance model as two objectives. The P E ratio is one of the important criteria for investment in the stock market, which captures the current expectations of the market activists about different companies. Experts’ Recommendations for different Market Sectors, on the other hand, captures the experts’ predictions about the future of the stock market. There are many solving methods for the portfolio optimization problem, but almost none of them investigates Invasive Weed Optimization algorithm (IWO). In this research, the proposed multi-objective portfolio selection model has been transformed into a single-objective programming model using fuzzy normalization and uniform design method. Some guidelines are given for parameter setting in the proposed IWO algorithm. The model is then applied to monthly data of top 50 companies of Tehran Stock Exchange Market in 2013. The proposed model is then solved by three methods: (1) the proposed IWO algorithm, (2) the Particle Swarm Optimization algorithm (PSO), and (3) the Reduced Gradient Method (RGM). The non-dominated solutions of these algorithms are compared with each other using Data Envelopment Analysis (DEA). According to the comparisons, it can be concluded that IWO and PSO algorithms have the same performance in most important criteria, but IWO algorithm has better solving time than PSO algorithm and better performance in dominating inefficient solutions, and PSO algorithm has better results in total violation of constraints.


      PubDate: 2016-01-16T05:54:31Z
       
  • Particle swarm and Box’s complex optimization methods to design linear
           tubular switched reluctance generators for wave energy conversion
    • Abstract: Publication date: Available online 7 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): R.P.G. Mendes, M.R.A. Calado, S.J.P.S. Mariano
      This paper addresses the optimization of the linear switched reluctance generator with tubular topology to be applied in a sea wave energy conversion system. Two new algorithms to optimize the geometry of the generators are proposed. The algorithms are based on both particle swarm and Box´s complex optimization methods. The optimization procedures consist of a multidimensional optimal value search. First the initial variable vectors are specified throughout the feasible search space. Then, an iterative procedure is applied with the goal of finding the variable values that minimize the objective function. The proposed algorithms are suitable for the optimization problem considered since the objective function is highly nonlinear and not analytically defined, as evaluated using a finite element analysis based software, and show very good performance. A factor that characterizes the generation capabilities is also defined and the obtained optimized generators are compared.


      PubDate: 2016-01-11T05:12:06Z
       
  • Structural damage detection using artificial bee colony algorithm with
           hybrid search strategy
    • Abstract: Publication date: Available online 7 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): Z.H. Ding, M. Huang, Z.R. Lu
      An approach for structural damage detection using the artificial bee colony(ABC) algorithm with hybrid search strategy based on modal data is presented. More search strategies are offered and the bee will choose one search mode based on the tournament selection strategy. And a kind of elimination mechanism is introduced to improve the convergence rate. A truss and a plate are studied as two numerical examples to illustrate the efficiency of proposed method. An experimental work on a beam is studied for further verification. Final results show the present method can acquire the better identification results, compared with those from GA, the original ABC and quick ABC (QABC) algorithm, even under some measurement noise.


      PubDate: 2016-01-11T05:12:06Z
       
  • A hybridization of an Improved Particle Swarm optimization and
           Gravitational Search Algorithm for Multi-Robot Path Planning
    • Abstract: Publication date: Available online 7 January 2016
      Source:Swarm and Evolutionary Computation
      Author(s): P.K Das, H.S. Behera, B.K. Panigrahi
      This paper proposed a new methodology to determine the optimal trajectory of the path for multi-robot in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with an improved Gravitational Search algorithm (IGSA). The proposed approach embedded the social essence of IPSO with motion mechanism of IGSA. The proposed hybridization IPSO-IGSA maintain the efficient balance between exploration and exploitation because of adopting co-evolutionary techniques to update the IGSA acceleration and particle positions with IPSO velocity simultaneously. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all robots to their respective destination in the environment. The robot on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO-IGSA. Finally the analytical and experimental results of the multi-robot path planning were compared to those obtained by IPSO-IGSA, IPSO, IGSA in a similar environment. The Simulation and the Khepera environment result show outperforms of IPSO-IGSA as compared with IPSO and IGSA with respect to optimize the path length from predefine initial position to designation position ,energy optimization in the terms of number of turn and arrival time.


      PubDate: 2016-01-11T05:12:06Z
       
 
 
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