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Journal Cover Swarm and Evolutionary Computation
  [SJR: 2.167]   [H-I: 22]   [2 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [3175 journals]
  • Agent-based game theoretic model for block motion estimation and its
           multicore implementation
    • Authors: Manal K. Jalloul; Mohamad Adnan Al-Alaoui
      Abstract: Publication date: Available online 26 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Manal K. Jalloul, Mohamad Adnan Al-Alaoui
      Motion estimation (ME) is one of the main tools employed for eliminating temporal redundancies in video coding. It is the most critical and time-consuming tool of the complete encoder and typically requires 60%–80% of the total computational time. Block-matching ME (BME) algorithms divide a frame into macroblocks (MB) and look for the best possible match in the reference frame. This paper introduces a novel parallel framework to speed up the BME process. This is done by introducing a novel level of parallelism within the MB. The problem of BME is cast in a non-cooperative game-theoretic setting and a distributed multi-agent system is employed to solve the problem. First, a given MB is divided into subblocks and an agent is defined for each subblock. Then, the problem is formulated as a Consensus game and our approximation of the global utility function for the MB is defined. Building on this, agents' utilities are derived so that the resulting game is a potential game. To solve the game, distributed sequential and simultaneous algorithms based on game-theoretic Best Response Dynamics (BRD) and particle swarm optimization (PSO) are presented. Each agent uses PSO as its local search engine to autonomously maximize the utility of its subblock and BRD drive the agents with minimum local communication towards the maximum of the global utility function of the whole MB. Experimental results show that these algorithms provide good estimation quality with low computational cost as compared to other techniques. Moreover, in addition to its decentralized and distributed nature, the simultaneous algorithm is also inherently parallel at the agents' level within the MB. A parallel implementation of this algorithm using the MATLAB Parallel Computing Toolbox™ (PCT) on a multicore system shows that speedup is indeed obtained.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.012
  • Optimal maintenance scheduling of generator units using discrete integer
           cuckoo search optimization algorithm
    • Authors: Srinivasan Lakshminarayanan; Devinder Kaur
      Abstract: Publication date: Available online 25 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Srinivasan Lakshminarayanan, Devinder Kaur
      In this paper, Discrete Integer Cuckoo Search (DICS) Optimization Algorithm is presented for generating an Optimal Maintenance Schedule for power utility with multiple generator units and complex constraints of Man Power Availability, Load Demand and strict Maintenance Window. The objective is to maximize and distribute the reserved power evenly across the fifty two weeks while satisfying the multiple constraints. This is a complex combinatorial NP-hard problem and there is no unique solution available for it. Nature inspired Cuckoo Search algorithm has been chosen to address this problem. Cuckoo search algorithm is a metaheuristic algorithm based on the obligate brood parasitism of cuckoo bird species, where cuckoo tries to find the best nest of other birds whose eggs resemble her own to lay her eggs to be hatched by other birds. Therefore the problem is formulated to find the best host nest. The host nest is defined according to the constraints of the power utility. The results obtained are compared with the work of previous researchers using the same test system and using the Genetic Algorithm with Binary Representation (GABR), Genetic Algorithm with Integer Representation (GAIR), Discrete Particle Swarm Optimization (DPSO), Modified Discrete Particle Swarm Optimization (MDPSO) and Hybrid Scatter Genetic Algorithm (HSGA). The results show that the DICS outperformed all the other algorithms.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.016
  • Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the
           interleaved multi-start scheme
    • Authors: Ngoc Hoang Luong; Han La Poutré; Peter A.N. Bosman
      Abstract: Publication date: Available online 15 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Ngoc Hoang Luong, Han La Poutré, Peter A.N. Bosman
      The Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA) has been shown to be a promising solver for multi-objective combinatorial optimization problems, obtaining an excellent scalability on both standard benchmarks and real-world applications. To attain optimal performance, MO-GOMEA requires its two parameters, namely the population size and the number of clusters, to be set properly with respect to the problem instance at hand, which is a non-trivial task for any EA practitioner. In this article, we present a new version of MO-GOMEA in combination with the so-called Interleaved Multi-start Scheme (IMS) for the multi-objective domain that eliminates the manual setting of these two parameters. The new MO-GOMEA is then evaluated on multiple benchmark problems in comparison with two well-known multi-objective evolutionary algorithms (MOEAs): Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experiments suggest that MO-GOMEA with the IMS is an easy-to-use MOEA that retains the excellent performance of the original MO-GOMEA.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.005
  • InDM2: Interactive Dynamic Multi-Objective Decision Making Using
           Evolutionary Algorithms
    • Authors: Antonio J. Nebro; Ana B. Ruiz; Cristóbal Barba-González; José García-Nieto; Mariano Luque; José F. Aldana-Montes
      Abstract: Publication date: Available online 14 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Antonio J. Nebro, Ana B. Ruiz, Cristóbal Barba-González, José García-Nieto, Mariano Luque, José F. Aldana-Montes
      Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi-objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how it is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.004
  • Make robots be bats: specializing robotic swarms to the Bat algorithm
    • Authors: Patricia Suárez; Andrés Iglesias; Akemi Gálvez
      Abstract: Publication date: Available online 14 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Patricia Suárez, Andrés Iglesias, Akemi Gálvez
      Bat algorithm is a powerful nature-inspired swarm intelligence method proposed by Prof. Xin-She Yang in 2010, with remarkable applications in industrial and scientific domains. However, to the best of authors' knowledge, this algorithm has never been applied so far in the context of swarm robotics. With the aim to fill this gap, this paper introduces the first practical implementation of the bat algorithm in swarm robotics. Our implementation is performed at two levels: a physical level, where we design and build a real robotic prototype; and a computational level, where we develop a robotic simulation framework. A very important feature of our implementation is its high specialization: all (physical and logical) components are fully optimized to replicate the most relevant features of the real microbats and the bat algorithm as faithfully as possible. Our implementation has been tested by its application to the problem of finding a target location within unknown static indoor 3D environments. Our experimental results show that the behavioral patterns observed in the real and the simulated robotic swarms are very similar. This makes our robotic swarm implementation an ideal tool to explore the potential and limitations of the bat algorithm for real-world practical applications and their computer simulations.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.005
  • Multiobjective evolutionary algorithms based on target region preferences
    • Authors: Longmei Li; Yali Wang; Heike Trautmann; Ning Jing; Michael Emmerich
      Abstract: Publication date: Available online 14 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Longmei Li, Yali Wang, Heike Trautmann, Ning Jing, Michael Emmerich
      Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.006
  • Vanishing point detection for self-driving car using harmony search
    • Authors: Yoon Young Moon; Zong Woo Geem; Gi-Tae Han
      Abstract: Publication date: Available online 13 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Yoon Young Moon, Zong Woo Geem, Gi-Tae Han
      Self-driving or autonomous vehicles require an ability to detect road lanes. In order to do so, a vanishing point should be first detected because the vanishing point exists on the extended lines of road lanes. For detecting the vanishing point, a random sample consensus (RANSAC) algorithm has been generally utilized. However, the performance of RANSAC is sometimes not so good but fluctuated. Thus, this study proposes a new approach to estimate the vanishing point using a harmony search (HS) algorithm. Results show that HS stably estimates vanishing points with respect to statistics when compared with RANSAC. We hope this model to be utilized in self-driving car in the future.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.007
  • A generic fuzzy approach for multi-objective optimization under
    • Authors: Oumayma Bahri; El-Ghazali Talbi; Nahla Ben Amor
      Abstract: Publication date: Available online 12 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Oumayma Bahri, El-Ghazali Talbi, Nahla Ben Amor
      Multi-objective optimization under uncertainty has gained considerable attention in recent years due to its practical applications in real-life. Many studies have been conducted on this topic, but almost all of them transformed the problem into a mono-objective one or just neglected the effects of uncertainty on the outcomes. This paper addresses specific uncertain multi-objective problems in which uncertainty is expressed by means of triangular fuzzy numbers. To handle these problems, we introduced a new approach able to solve them without any transformation by considering fuzziness propagation to the objective functions. The proposed approach is composed of two main contributions: First, a fuzzy Pareto dominance is defined for ranking the generated fuzzy solutions. Second, a generic fuzzy extension of well-known evolutionary algorithms is suggested as resolution methods. An experimental study on multi-objective Vehicle Routing Problems (VRP) with uncertain demands is finally carried to evaluate our approach.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.002
  • A modified PBI approach for multi-objective optimization with complex
           Pareto fronts
    • Authors: Qisheng Zhang; Wen Zhu; Bo Liao; Xiangtao Chen; Lijun Cai
      Abstract: Publication date: Available online 12 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Qisheng Zhang, Wen Zhu, Bo Liao, Xiangtao Chen, Lijun Cai
      The penalty-based boundary intersection (PBI) approach is widely used in the decomposition-based multi-objective evolutionary algorithm (MOEA/D). Generally, a uniform distribution of weight vectors in PBI approach will lead to a set of evenly distributed solutions on the Pareto-optimal front (POF), but this approach cannot work well in practice when the target multi-objective optimization problem (MOP) has a complex POF. For example, the POF may have disconnected regions and a long tail and a sharp peak and a degenerate geometry, which significantly degrades the performance of the original MOEA/D. This paper proposes a modified PBI (MPBI) approach and a strategy of adjusting reference points (ARP) to handle these MOPs with complex fronts. A two-stage strategy is adopted in the proposed algorithm. The first stage is to determine a hyperplane based on the modified PBI approach, so that the projection points derived from the solutions obtained in second stage to this hyperplane are all in the first quadrant. Exploring those regions where the solution exists is also a key task in this stage. The second stage is to adjust the reference points periodically so that the reference points can be redistributed adaptively to improve the distribution of solutions. The framework of the proposed algorithm is based on θ -DEA and named NSGA-MPBI. Some widely used test instances and three many-objective MOPs with complex POFs are employed in the experiments. The experimental results indicate that NSGA-MPBI outperforms the state-of-the-art algorithms.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.001
  • A comparison study of harmony search and genetic algorithm for the max-cut
    • Authors: Yong-Hyuk Kim; Yourim Yoon; Zong Woo Geem
      Abstract: Publication date: Available online 9 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Yong-Hyuk Kim, Yourim Yoon, Zong Woo Geem
      The max-cut problem is one of well-known NP-complete problems and has applications in various fields such as the design process for VLSI (Very-Large-Scale Integration) chips and spin glass theory in statistical physics. In this paper, a harmony search algorithm for the max-cut problem is proposed. Compared to genetic algorithm, harmony search algorithm has advantages of generating a new vector after considering all of the existing vectors and requiring only a few number of parameters to be determined before the run of the algorithm. For 31 benchmark graphs of various types, the proposed harmony search algorithm is compared with a newly developed genetic algorithm and another genetic algorithm taken from the recent literature are presented. The proposed harmony search algorithm produced significantly better results than the two genetic algorithms.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.004
  • A two-stage three-machine assembly flow shop scheduling with learning
           consideration to minimize the flowtime by six hybrids of particle swarm
    • Authors: Chin-Chia Wu; Jia-Yang Chen; Win-Chin Lin; Kunjung Lai; Shang-Chia Liu; Pay-Wen Yu
      Abstract: Publication date: Available online 8 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Chin-Chia Wu, Jia-Yang Chen, Win-Chin Lin, Kunjung Lai, Shang-Chia Liu, Pay-Wen Yu
      There have been many applications of two-stage three-machine assembly flow shop in query scheduling, such as fire engine assembly, personal computer manufacturing, and distributed database system. Moreover, learning phenomenon has been shown present in many two-stage assembly flow shop environments. In conjunction with this learning phenomenon, we addressed, in this study, a two-stage three-machine flow shop scheduling problem with a cumulated learning function. Our objective was to search an optimal sequence for minimizing the flowtime (or total completion time). We developed some dominance propositions with a lower bound used in a branch-and-bound algorithm for small-size jobs. We also proposed six versions of hybrid particle swam optimization (PSO) algorithms to find approximate solutions for small-size and big-size jobs, and for three different data types. In addition, analysis of variance (ANOVA) was employed to examine the performances of the six PSOs for each data type. Subsequently, Fisher's least significant difference tests were carried out to further make pairwise comparisons among the performances of the six algorithms.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.012
  • Bio-inspired methods modeled for respiratory disease detection from
           medical images
    • Authors: Marcin Woźniak; Dawid Połap
      Abstract: Publication date: Available online 8 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Marcin Woźniak, Dawid Połap
      Medicine is an important venue for practical applications of science. A fusion of mathematical modeling and programming into computer methods makes a great support for efficient treatment and diagnosis. Computational Intelligence is one of these sciences which bring valuable help in decision support. In this article we present a devoted methodology implemented to simulate medical examinations of pulmonary diseases. We propose Bio-Inspired Methods modeled to work as the automated decision support in a process of diseased tissues detection over input x-ray images. These methods have special features that with devoted modeling make them independently search over the images with a good accuracy. In our approach we use dedicated fitness condition for selected heuristic algorithms. Mathematical model of medical expertise is formulated as a function used to search for special features of pixels that are representing respiratory diseases like pneumonia, lungs sarcoidosis and cancer. Presented decision modeling simulates medical x-ray image examination process to show where potentially diseased tissues are located. To enhance decision support the system returns to the doctor detection results from two tracks. In the first, patient and doctor can see detection from each of the algorithms, and in the second aggregated results. In this way the doctor receives a complex support that simulates consulting the image with various specialists. In benchmark tests, for a set of original x-ray images from various clinics, applied methods were examined to demonstrate benefits of using implemented solution. Results show that proposed methodology is efficient and promising for pulmonary diseases detection.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.008
  • Finding influential users for different time bounds in social networks
           using multi-objective optimization
    • Authors: Azadeh Mohammadi; Mohamad Saraee
      Abstract: Publication date: Available online 8 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Azadeh Mohammadi, Mohamad Saraee
      Online social networks play an important role in marketing services. Influence maximization is a major challenge, in which the goal is to find the most influential users in a social network. Increasing the number of influenced users at the end of a diffusion process while decreasing the time of diffusion are two main objectives of the influence maximization problem. The goal of this paper is to find multiple sets of influential users such that each of them is the best set to spread influence for a specific time bound. Considering two conflicting objectives, increasing influence and decreasing diffusion time, we employ the NSGA-II algorithm which is a powerful algorithm in multi-objective optimization to find different seed sets with high influence at different diffusion times. Since social networks are large, computing influence and diffusion time of all chromosomes in each iteration will be challenging and computationally expensive. Therefore, we propose two methods which can estimate the expected influence and diffusion time of a seed set in an efficient manner. Providing the set of all potentially optimal solutions helps a decision maker evaluate the trade-offs between the two objectives, i.e., the number of influenced users and diffusion time. In addition, we develop an approach for selecting seed sets, which have optimal influence for specific time bounds, from the resulting Pareto front of the NSGA-II. Finally, we show that applying our algorithm to real social networks outperforms existing algorithms for the influence maximization problem. The results show a good compromise between the two objectives and the final seed sets result in high influence for different time bounds.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.02.003
  • A survey of swarm intelligence for portfolio optimization: Algorithms and
    • Authors: Okkes Ertenlice; Can B. Kalayci
      Abstract: Publication date: Available online 3 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Okkes Ertenlice, Can B. Kalayci
      In portfolio optimization (PO), often, a risk measure is an objective to be minimized or an efficient frontier representing the best tradeoff between return and risk is sought. In order to overcome computational difficulties of this NP-hard problem, a growing number of researchers have adopted swarm intelligence (SI) methodologies to deal with PO. The main PO models are summarized, and the suggested SI methodologies are analyzed in depth by conducting a survey from the recent published literature. Hence, this study provides a review of the SI contributions to PO literature and identifies areas of opportunity for future research.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.009
  • Robust tuning of excitation controller for stability enhancement using
           multi-objective metaheuristic Firefly algorithm
    • Authors: Mahesh Singh; R.N. Patel; D.D. Neema
      Abstract: Publication date: Available online 1 February 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Mahesh Singh, R.N. Patel, D.D. Neema
      The proposed approach focuses on investigating the optimum values of Power System Stabilizer (PSS) parameters by the implementation of Firefly algorithm (FFA) based optimization technique. It minimizes the low frequency oscillations such that both maximum overshoot and settling time are reduced simultaneously, since the reduction of both these parameters will considerably improve the stability of the power system. In this paper, eigenvalue and overshoot based multi objective function is used to enhance damping of electromechanical oscillations in the system. Firstly, the conventional lead-lag structure of PSS, which has its design based on phase compensation technique, was applied to the systems under study. Then, Firefly optimization technique is implemented on three different standard test systems and a comparative analysis is carried out with the classical techniques (under the disturbances). Moreover, the performance of FFA tuned PSS is also compared with PSS tuned using Genetic algorithm (GA). Based on the simulations, it is seen that Firefly optimization technique based PSS converges faster as compared to conventional PSS and GAPSS. Thus, the implementation and evaluation of firefly algorithm has emerged as an evolving platform and can be considered as a very impressive catalytic method to tune the PSS parameters.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.010
  • Multi objective evolutionary algorithm for designing energy efficient
           distribution transformers
    • Authors: S. Tamilselvi; S. Baskar; L. Anandapadmanaban; V. Karthikeyan; S. Rajasekar
      Abstract: Publication date: Available online 31 January 2018
      Source:Swarm and Evolutionary Computation
      Author(s): S. Tamilselvi, S. Baskar, L. Anandapadmanaban, V. Karthikeyan, S. Rajasekar
      This paper has solved the transformer design optimization problem using Multi-Objective Evolutionary Algorithms based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA). For lesser computation burden, the existing design techniques merely employ few Standard Design Variables (SDV), satisfying only a few performance constraints, resulting in an approximated design, without any focus on an energy efficient transformer. The proposed methodology minimizes four sets of conflicting design bi-objectives, subjected to 27 constraints, incorporating three crucial design variables with SDV to ensure energy efficient transformer design with lesser losses, total life time cost (TLTC), green house gas emission, and failure rate. Different cases are analysed on a sample 1500 kV A transformer, which is designed by existing technique and the proposed multi objective optimization problem formulation approach and the performances of the competing transformers are compared. To prove the effectiveness of Iterative Chaotic map with infinite collapses assisted MOEA/D-DRA (ICMDRA), NSGA-II has also been successfully applied to solve the problem. When tested in all three different rating transformers, the simulation results have proved that the proposed methodology saves energy, cost, and material, with ICMDRA rather than NSGA-II. This paper identifies ICMDRA as a superior algorithm for transformer design, in terms of diversity and convergence. Also, the core loss calculation of the transformer designed using the proposed methodology is validated by 3D-FEM assessment and experimental prototype setup for a 200 kV A transformer.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.007
  • Chaos-enhanced mobility models for multilevel swarms of UAVs
    • Authors: Martin Rosalie; Grégoire Danoy; Serge Chaumette; Pascal Bouvry
      Abstract: Publication date: Available online 31 January 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Martin Rosalie, Grégoire Danoy, Serge Chaumette, Pascal Bouvry
      The number of civilian and military applications using Unmanned Aerial Vehicles (UAVs) has increased during the last years and the forecasts for upcoming years are exponential. One of the current major challenges consist in considering UAVs as autonomous swarms to address some limitations of single UAV usage such as autonomy, range of operation and resilience. In this article we propose novel mobility models for multi-level swarms of collaborating UAVs used for the coverage of a given area. These mobility models generate unpredictable trajectories using a chaotic solution of a dynamical system. We detail how the chaotic properties are used to structure the exploration of an unknown area and enhance the exploration part of an Ant Colony Optimization method. Empirical evidence of the improvement of the coverage efficiency obtained by our mobility models is provided via simulation. It clearly outperforms state-of-the-art approaches.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.002
  • Comparison of mutation strategies in Differential Evolution – A
           probabilistic perspective
    • Authors: Karol Opara; Jarosław Arabas
      Abstract: Publication date: Available online 13 January 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Karol Opara, Jarosław Arabas
      Differential Evolution (DE) is a state-of-the art evolutionary algorithm that solves global optimization problems in a real domain. The algorithm adapts the mutation range and direction by basing these on the differences between individuals in the current population. In this paper, we provide formulas for the expectation vectors and covariance matrices of the mutants' distribution for several operators of differential mutation. The covariance matrices are proportional to each other, which means that the main difference between the analyzed DE operators is the mutation range. This can be conveniently described using a generalized scaling factor g ( F ) , introduced in this paper. Next, we propose transformations of the scaling factors that make the expectation vectors and covariance matrices of the expected mutants' distributions equal to the respective statistics of DE/best/1 or DE/rand/1. These transformations establish a framework for a synthetic investigation of various differential mutation operators and for generalizing the results of parameter tuning. A simulation study based on the CEC′13 benchmark in 10, 30 and 50 dimensions confirms that the transformations do not influence the performance much, especially in the case of DE operators with two difference vectors.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2017.12.007
  • Optimal parameter regions and the time-dependence of control parameter
           values for the particle swarm optimization algorithm
    • Authors: Kyle Robert Harrison; Andries P. Engelbrecht; Beatrice M. Ombuki-Berman
      Abstract: Publication date: Available online 11 January 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Kyle Robert Harrison, Andries P. Engelbrecht, Beatrice M. Ombuki-Berman
      The particle swarm optimization (PSO) algorithm is a stochastic search technique based on the social dynamics of a flock of birds. It has been established that the performance of the PSO algorithm is sensitive to the values assigned to its control parameters. Many studies have examined the long-term behaviours of various PSO parameter configurations, but have failed to provide a quantitative analysis across a variety of benchmark problems. Furthermore, two important questions have remained unanswered. Specifically, the effects of the balance between the values of the acceleration coefficients on the optimal parameter regions, and whether the optimal parameters to employ are time-dependent, warrant further investigation. This study addresses both questions by examining the performance of a global-best PSO using 3036 different parameter configurations on a set of 22 benchmark problems. Results indicate that the balance between the acceleration coefficients does impact the regions of parameter space that lead to optimal performance. Additionally, this study provides concrete evidence that, for the examined problem dimensions, larger acceleration coefficients are preferred as the search progresses, thereby indicating that the optimal parameters are, in fact, time-dependent. Finally, this study provides a general recommendation for the selection of PSO control parameter values.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.006
  • Stability analysis of Artificial Bee Colony optimization algorithm
    • Authors: Jagdish Chand Bansal; Anshul Gopal; Atulya K. Nagar
      Abstract: Publication date: Available online 10 January 2018
      Source:Swarm and Evolutionary Computation
      Author(s): Jagdish Chand Bansal, Anshul Gopal, Atulya K. Nagar
      Theoretical analysis of swarm intelligence and evolutionary algorithms is relatively less explored area of research. Stability and convergence analysis of swarm intelligence and evolutionary algorithms can help the researchers to fine tune the parameter values. This paper presents the stability analysis of a famous Artificial Bee Colony (ABC) optimization algorithm using von Neumann stability criterion for two-level finite difference scheme. Parameter selection for the ABC algorithm is recommended based on the obtained stability conditions. The findings are also validated through numerical experiments on test problems.

      PubDate: 2018-02-26T15:57:02Z
      DOI: 10.1016/j.swevo.2018.01.003
  • A survey on multi-objective evolutionary algorithms for the solution of
           the environmental/economic dispatch problems
    • Authors: B.Y. Qu; Y.S. Zhu; Y.C. Jiao; M.Y. Wu; P.N. Suganthan; J.J. Liang
      Pages: 1 - 11
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): B.Y. Qu, Y.S. Zhu, Y.C. Jiao, M.Y. Wu, P.N. Suganthan, J.J. Liang
      Development of efficient multi-objective evolutionary algorithms (MOEAs) has provided effective tools to solve environmental/economic dispatch (EED) problems. EED is a highly constrained complex bi-objective optimization problem. Since 1990s, numerous publications have reported the applications of MOEAs to solve the EED problems. This paper surveys the state-of-the-art of research related to this direction. It covers topics of typical MOEAs, classical EED problems, Dynamic EED problems, EED problems incorporating wind power, EED problems incorporating electric vehicles and EED problems within micro-grids. In addition, some potential directions for future research are also presented.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.06.002
      Issue No: Vol. 38 (2017)
  • An overview and comparative analysis of recent bio-inspired optimization
           techniques for wind integrated multi-objective power dispatch
    • Authors: Hari Mohan Dubey; Manjaree Pandit; B.K. Panigrahi
      Pages: 12 - 34
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Hari Mohan Dubey, Manjaree Pandit, B.K. Panigrahi
      Over the last few decades, bio-inspired (BI) evolutionary optimization techniques have experienced overwhelming popularity, extraordinary growth and large number of applications, particularly, in the field of engineering and technology. These techniques present a tough competition to traditional numerical methods which suffer from convexity and continuity assumptions and which normally employ a gradient based search that is sensitive to the initial solution. While initial BI techniques suffered from limitations such as premature convergence and dependence on control parameters, eventually, these issues were specifically addressed by improved variants and many novel BI methods. The population based computing methods are particularly attractive for solving multi-objective (MO) problems due to their capability of producing a large number of Pareto-optimal solutions in one run. In this paper, an integrated ranking index (IRI) composed of TOPSIS and fuzzy-min concept is proposed as a performance metrics to aggregate the different objectives. The performance of eight handpicked recent BI techniques is compared for the solution of wind integrated multi-objective optimal power dispatch (MOOD) problem for simultaneous minimization of fuel cost and emission. Due to the uncertain nature of wind power (WP), the effect of its over and underestimation on both economic as well as environmental aspects, has also been considered. Six standard test cases having non-convex, multi-modal and discontinuous objective functions, dynamic operation and complex equality/inequality constraints, are selected for testing Flower Pollination Algorithm (FPA), Mine Blast Algorithm (MBA), Backtracking Search Algorithm (BSA), Symbiotic Organisms Search (SOS), Ant Lion Optimizer (ALO), Moth-Flame Optimization (MFO), Stochastic Fractal Search (SFS) and Lightning Search Algorithm (LSA).

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.012
      Issue No: Vol. 38 (2017)
  • Evolutionary heterogeneous clustering for rating prediction based on user
           collaborative filtering
    • Authors: Jianrui Chen; Uliji; Hua Wang; Zaizai Yan
      Pages: 35 - 41
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Jianrui Chen, Uliji, Hua Wang, Zaizai Yan
      Recommender systems play an important role in our life, which would help users to find what they are interested in. Collaborative filtering is the most widely used and successful method for personalized recommendation. In this paper, a novel heterogeneous evolutionary clustering is presented. The goal of our algorithm is to gather users with similar interest into the same cluster and to help users find items that fit their personal tastes best. The suggestions from friends with similar interest may be adopted with high probability. Firstly, items and users are regarded as heterogeneous individuals in the network. According to the constructed network model, states of individuals evolve over time. Individuals with higher scores would cluster into together and individuals with lower scores would get away. After many iterations, states of items and users would be stable. In light of stable states of heterogeneous individuals, they are clustered into several groups. Secondly, user-based collaborative filtering are adopted in each cluster. Similarities between individuals only in same cluster are computed not for all individuals in system. The target rating is calculated according to user-based collaborative filtering in its cluster. Diverse simulations show the efficiency of our proposed methods. Moreover, the presented method gains better prediction results than two existing preferable algorithms.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.05.008
      Issue No: Vol. 38 (2017)
  • An effective invasive weed optimization algorithm for scheduling
           semiconductor final testing problem
    • Authors: Hong-Yan Sang; Pei-Yong Duan; Jun-Qing Li
      Pages: 42 - 53
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Hong-Yan Sang, Pei-Yong Duan, Jun-Qing Li
      In this paper, we address a semiconductor final testing problem from the semiconductor manufacturing process. We aim to determine both the assignment of machines and the sequence of operations on all the machines so as to minimize makespan. We present a cooperative co-evolutionary invasive weed optimization (CCIWO) algorithm which iterates with two coupled colonies, one of which addresses the machine assignment problem and the other deals with the operation sequence problem. To well balance the search capability of the two colonies, we adopt independent size setting for each colony. We design the reproduction and spatial dispersal methods for both the colonies by taking advantage of the information collected during the search process and problem-specific knowledge. Extensive experiments and comparison show that the proposed CCIWO algorithm performs much better than the state-of-the-art algorithms in the literature for solving the semiconductor final testing scheduling problem with makespan criteria.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.05.007
      Issue No: Vol. 38 (2017)
  • A knowledge-guided multi-objective fruit fly optimization algorithm for
           the multi-skill resource constrained project scheduling problem
    • Authors: Ling Wang; Xiao-long Zheng
      Pages: 54 - 63
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Ling Wang, Xiao-long Zheng
      In this paper, a knowledge-guided multi-objective fruit fly optimization algorithm (MOFOA) is proposed for the multi-skill resource-constrained project scheduling problem (MSRCPSP) with the criteria of minimizing the makespan and the total cost simultaneously. First, a solution is represented by two lists, i.e. resource list and task list. Second, the minimum total cost rule is designed for the initialization according to the property of the problem. Third, the smell-based search is implemented via the neighborhood based search operators that are specially designed for the MSRCPSP, while the vision-based search adopts the technique for the order preference by similarity to an ideal solution (TOPSIS) and the non-dominated sorting collaboratively to complete the multi-objective evaluation. In addition, a knowledge-guided search procedure is introduced to enhance the exploration of the FOA. Finally, the design-of-experiment (DOE) method is used to investigate the effect of parameter setting, and numerical tests based on benchmark instances are carried out. The results compared to other algorithms demonstrate the effectiveness of the MOFOA with knowledge-guided search in solving the multi-objective MSRCPSP.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.06.001
      Issue No: Vol. 38 (2017)
  • An improved migrating birds optimization for an integrated lot-streaming
           flow shop scheduling problem
    • Authors: Tao Meng; Quan-Ke Pan; Jun-Qing Li; Hong-Yan Sang
      Pages: 64 - 78
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Tao Meng, Quan-Ke Pan, Jun-Qing Li, Hong-Yan Sang
      Lot-streaming is an effective technology to enhance the production efficiency by splitting a job or a lot into several sublots. It is commonly assumed that lot-splitting (i.e. job-splitting) is specified in advance and fixed during the optimization procedure in recent studies on lot-streaming flow shop scheduling problems. In many real-world production processes, however, it is not easy to determine the optimal lot-splitting beforehand. Therefore, in this paper we consider an integrated lot-streaming flow shop scheduling problem in which lot-splitting and job scheduling are needed to be optimized simultaneously. We provide a mathematical model for the problem and present an improved migrating birds optimization (IMMBO) to minimize the maximum completion time or makespan. In the IMMBO algorithm, a harmony search based scheme is designed to construct neighborhood of solutions, which makes good use of optimization information from the population and can tune the search scope adaptively. Moreover, a leaping mechanism is introduced to avoid being trapped in the local optimum. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify the effectiveness of the proposed IMMBO algorithm.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.06.003
      Issue No: Vol. 38 (2017)
  • Adaptive artificial immune networks for mitigating DoS flooding attacks
    • Authors: Jorge Maestre Vidal; Ana Lucila Sandoval Orozco; Luis Javier García Villalba
      Pages: 94 - 108
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba
      Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD’99, CAIDA’07 and CAIDA’08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.002
      Issue No: Vol. 38 (2017)
  • Accelerated multi-gravitational search algorithm for size optimization of
           truss structures
    • Authors: Mohsen Khatibinia; Hessam Yazdani
      Pages: 109 - 119
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Mohsen Khatibinia, Hessam Yazdani
      Weak local exploitation capability of the gravitational search algorithm (GSA) and its slow convergence rate in final iterations have been demonstrated in the literature. This paper presents a modified GSA denoted here as the accelerated multi-gravitational search algorithm (AMGSA) that exhibits an improved convergence rate. In AMGSA, the simplex crossover (SPX) and the operator mutation of the breeder genetic algorithm (BGA) are incorporated with the multi-gravitational search algorithm (MGSA) to achieve an algorithm with a good exploration-exploitation balance. MGSA is adopted to prevent stagnation of the search into a local optimum (i.e. to improve the exploration capability), while the SPX and the BGA mutation operator are used to bias the search toward promising areas of the search space (i.e. to promote local exploitation). The performance of AMGSA is evaluated using several benchmark truss optimization examples. Results indicate that AMGSA not only exhibits an improved balance between the exploration and exploitation schemes but also shows competitive promise in effectively and efficiently solving large-scale optimization problems as it requires a significantly lower number of structural analyses compared to other algorithms that it is checked against.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.001
      Issue No: Vol. 38 (2017)
  • A novel differential particle swarm optimization for parameter selection
           of support vector machines for monitoring metal-oxide surge arrester
    • Authors: Thi Thom Hoang; Ming-Yuan Cho; Mahamad Nabab Alam; Quoc Tuan Vu
      Pages: 120 - 126
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Thi Thom Hoang, Ming-Yuan Cho, Mahamad Nabab Alam, Quoc Tuan Vu
      Since metal-oxide surge arresters are the important over-voltage protection equipments used in power systems, their operating conditions must be monitored on a timely basis to give an alarm as soon as possible in order to increase the reliability of a power system. The paper proposes a novel differential particle swarm optimization-based (DPSO-based) support vector machine (SVM) classifier for the purpose of monitoring the surge arrester conditions. A DPSO-based technique is investigated to give better results, which optimizes the parameters of SVM classifiers. Three features are extracted as input vectors for evaluating five arrester conditions, including normal (N), pre-fault (A), tracking (T), abnormal (U) and degradation (D). Meanwhile, a comparative study of fault diagnosis is carried out by using a DPSO-based ANN classifier. The results obtained using the proposed method are compared to those obtained using genetic algorithm (GA) and particle swarm optimization (PSO). The experiments using an actual dataset will expectably show the superiority of the proposed approach in improving the performance of the classifiers.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.006
      Issue No: Vol. 38 (2017)
  • Maximum likelihood estimation for the parameters of skew normal
           distribution using genetic algorithm
    • Authors: Abdullah Yalçınkaya; Birdal Şenoğlu; Ufuk Yolcu
      Pages: 127 - 138
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Abdullah Yalçınkaya, Birdal Şenoğlu, Ufuk Yolcu
      Skew Normal (SN) distribution is widely used for modeling data sets having near normal and skew distribution. Maximum likelihood (ML) is the most popular method used to obtain estimators of model parameters. However, likelihood equations do not have explicit solutions in the context of SN. Therefore, we use the Genetic Algorithm (GA) which is a well known search technique inspired by the principles of biological systems, such as evolution, mutation and suchlike, to overcome problems encountered in solving likelihood equations. The GA has routinely high performance where traditional search techniques fail. We compare the efficiencies of ML estimators of model parameters using the GA with corresponding ML estimators obtained using other iterative techniques, such as Newton-Raphson (NR), Nelder Mead (NM), and Iteratively Re-weighting Algorithm (IRA). Simulation results show that ML estimators using the GA of the parameters of SN distribution are the most efficient among others with respect to bias, mean square error (MSE) and deficiency (Def) criteria.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.007
      Issue No: Vol. 38 (2017)
  • Evolutionary algorithms based synthesis of low sidelobe hexagonal arrays
    • Authors: Sudipta Das; Rajesh Bera; Durbadal Mandal; Sakti Prasad Ghoshal; Rajib Kar
      Pages: 139 - 157
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Sudipta Das, Rajesh Bera, Durbadal Mandal, Sakti Prasad Ghoshal, Rajib Kar
      In this work a procedure namely Findpeaks2 is proposed to detect the maximum sidelobe level (SLL) from the samples of three dimensional radiation pattern. This procedure detects all sidelobe peaks form the samples of the radiation pattern in the entire visible region. For illustration, a low sidelobe radiation pattern synthesis problem is formulated for two concentric regular hexagonal antenna array (CRHAA) geometries, having 6- and 8- rings. To verify the extent of applicability of the proposed procedure, both broadside and scanned array configurations are considered. Feed current amplitudes are considered as the optimizing variables. Two variations of current distributions are considered, i) identical feed for all the elements on a ring (hence the one variable per ring needs to be optimized), and ii) asymmetric excitation distribution (set of excitation amplitude of all elements as optimizing variables). The design objective has been considered to optimize the radiation patterns with very low interference from the entire sidelobe region. To restrict the fall of directivity value, a constraint on the lower limit of directivity value is considered. The impacts of symmetry and the constraint on directivity on the search of these algorithms are studied. Evolutionary algorithms like Real Coded Genetic Algorithm (RGA), Firefly Algorithm (FFA), Flower Pollination Algorithm (FPA), an adaptive variant of Particle Swarm Optimization Algorithm namely (APSO), and two recently proposed variants of DE namely Exponentially Weighted Moving Average Differential Evolution (EWMA-DE), and Differential Evolution with Individual Dependent Mechanism (IDE) are employed for this pattern optimization problem.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.003
      Issue No: Vol. 38 (2017)
  • DABE: Differential evolution in analogy-based software development effort
    • Authors: Tirimula Rao Benala; Rajib Mall
      Pages: 158 - 172
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Tirimula Rao Benala, Rajib Mall
      Several feature weight optimization techniques have been proposed for similarity functions in analogy-based estimation (ABE); however, no consensus regarding the method and settings suitable for producing accurate estimates has been reached. We investigate the effectiveness of differential evolution (DE) algorithm, for optimizing the feature weights of similarity functions of ABE by applying five successful mutation strategies. We have named this empirical analysis as DE in analogy-based software development effort estimation (DABE). We have conducted extensive simulation study on the PROMISE repository test suite to estimate the effectiveness of our proposed DABE technique. We find significant improvements in predictive performance of our DABE technique over ABE, particle swarm optimization-based feature weight optimization in ABE, genetic algorithm-based feature weight optimization in ABE, self-adaptive DE-based feature weight optimization ABE, adaptive differential evolution with optional external archive-based feature weight optimization ABE, functional link artificial neural network,artificial neural network with back propagation learning based software development effort estimation (SDEE), and radial basis function-based SDEE.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.009
      Issue No: Vol. 38 (2017)
  • A Robust Stochastic Fractal Search approach for optimization of the
           surface grinding process
    • Authors: Soheyl Khalilpourazari; Saman Khalilpourazary
      Pages: 173 - 186
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Soheyl Khalilpourazari, Saman Khalilpourazary
      Grinding process is one of the most important machining processes in industry. The mathematical model of the optimization of the grinding process includes three objective functions and a weighted objective function with a set of operational constraints. Due to nonlinearity and complexity of the mathematical model, optimization of grinding process is a challenging task. This paper aims to optimize the surface grinding process parameters to increase final surface quality and production rate while minimizing total process costs. A novel Robust Stochastic Fractal Search is proposed to solve the problem efficiently. To increase the efficiency of the algorithm, a robust design methodology named Taguchi method is utilized to tune the parameters of the Stochastic Fractal Search. Since, the basic version of the Stochastic Fractal Search is proposed for unconstrained optimization, in this research, an efficient constraint handling method is implemented to handle complex nonlinear constraints of the problem. To Show the applicability and efficiency of the proposed Robust Stochastic Fractal Search, an experimental example is solved and compared to the results of the previous researches in the literature as well as two novel algorithm MPEDE and HCLPSO. The results revealed that the Robust Stochastic Fractal Search provides very competitive solutions and outperforms other solution methods.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.008
      Issue No: Vol. 38 (2017)
  • A multi-objective evolutionary artificial bee colony algorithm for
           optimizing network topology design
    • Authors: Amani Saad; Salman A. Khan; Amjad Mahmood
      Pages: 187 - 201
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Amani Saad, Salman A. Khan, Amjad Mahmood
      The topological design of a computer communication network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple design objectives and numerous design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological design of distributed local area networks (DLANs). Five design objectives are considered herein, namely, network reliability, network availability, average link utilization, monetary cost, and network delay. Goal programming (GP) is incorporated to aggregate the multiple design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.010
      Issue No: Vol. 38 (2017)
  • On maximizing reliability of grid transaction processing system
           considering balanced task allocation using social spider optimization
    • Authors: Dharmendra Prasad Mahato; Ravi Shankar Singh
      Pages: 202 - 217
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Dharmendra Prasad Mahato, Ravi Shankar Singh
      This paper deals with the problem of task allocation in the grid transaction processing system. There has been quite some research on the development of tools and techniques for grid computing systems, yet some important issues, e.g., service reliability with load balanced transaction allocation in grid computing system, have not been sufficiently studied. Load balanced transaction allocation becomes a challenging job in such a complex and dynamic environment as both the application and computational resources are heterogeneous. The problem is further complicated by the fact that these resources may fail at any point of time. The problem of finding an optimal task allocation solution is known to be an NP-hard. We propose grid transaction allocation based on social spider optimization (LBGTA_SSO) method for this problem. The LBGTA_SSO is based on the cooperative behavior of social spiders to find a collection of task allocation solutions. We also derive reliability formulae for grid transactions considering resource availability. For comparison we modify some existing algorithms to obtain the task allocation algorithms. The results show that our algorithm works better than the modified existing algorithms.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.07.011
      Issue No: Vol. 38 (2017)
  • Weibull-based scaled-differences schema for Differential Evolution
    • Authors: Miguel
      Abstract: Publication date: February 2018
      Source:Swarm and Evolutionary Computation, Volume 38
      Author(s): Miguel Cárdenas-Montes
      Differential Evolution is one of the most efficient real-parameter optimization algorithm. It is based on the application of the scaled difference of a pair of population members to another population member, all of them distinct. Diverse variants have been proposed within this schema. In this work, the statistical distribution of these differences of high-performance variants of Differential Evolution is modelled through a Weibull probability distribution. From the application of this model to diverse Differential Evolution variants and benchmark functions, a pattern for the most efficient variants can be drawn. As a consequence, a variant where the scaled differences are replaced by random numbers generated from a Weibull distribution is proposed and evaluated.

      PubDate: 2017-12-27T13:32:09Z
  • Optimal placement of TCSC and SVC for reactive power planning using Whale
           optimization algorithm
    • Authors: Saurav Raj; Biplab Bhattacharyya
      Abstract: Publication date: Available online 23 December 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Saurav Raj, Biplab Bhattacharyya
      In the present work, Whale optimization algorithm (WOA), Differential evolution (DE), Grey wolf optimization (GWO), Quasi-opposition based Differential Evolution (QODE) and Quasi-opposition based Grey wolf optimization (QOGWO) algorithm has been applied for the solution of reactive power planning with FACTS devices i.e., Thyristor controlled series compensator (TCSC) and Static Var compensator (SVC). WOA is a recently developed nature-inspired meta-heuristic algorithm based on hunting behaviour of Humpback Whales; DE is a stochastic real-parameter optimization technique comprising of genetic parameters namely - mutation & cross-over; and GWO is a nature-inspired meta-heuristic algorithm based on hunting behaviour of Grey wolf. Standard IEEE 30 and IEEE 57 bus test system has been adopted for the testing purposes. Location of TCSC has been determined by the power flow analysis method and location of SVC has been determined by the voltage collapse proximity indication (VCPI) method. Further, WOA, GWO, DE, QODE and QOGWO algorithms have been applied to find the optimal setting of all control variables including TCSC, the series type and SVC, the shunt kind of FACTS device in the test system which minimizes active power loss and system operating cost while maintaining voltage profile within permissible limit. The superiority of the proposed WOA technique has been illustrated by comparing the results obtained with all other techniques discussed in the present problem. ANOVA test has also been conducted to show the statistical analysis between different techniques. The proposed approach shows lesser number of iterations which does not gets trapped in the local minima and offers promising convergence characteristics.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.12.008
  • Profit maximizing logistic model for customer churn prediction using
           genetic algorithms
    • Authors: Eugen Stripling; Seppe vanden Broucke; Katrien Antonio; Bart Baesens; Monique Snoeck
      Abstract: Publication date: Available online 21 December 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck
      To detect churners in a vast customer base, as is the case with telephone service providers, companies heavily rely on predictive churn models to remain competitive in a saturated market. In previous work, the expected maximum profit measure for customer churn (EMPC) has been proposed in order to determine the most profitable churn model. However, profit concerns are not directly integrated into the model construction. Therefore, we present a classifier, named ProfLogit, that maximizes the EMPC in the training step using a genetic algorithm, where ProfLogit's interior model structure resembles a lasso-regularized logistic model. Additionally, we introduce threshold-independent recall and precision measures based on the expected profit maximizing fraction, which is derived from the EMPC framework. Our proposed technique aims to construct profitable churn models for retention campaigns to satisfy the business requirement of profit maximization. In a benchmark study with nine real-life data sets, ProfLogit exhibits the overall highest, out-of-sample EMPC performance as well as the overall best, profit-based precision and recall values. As a result of the lasso resemblance, ProfLogit also performs a profit-based feature selection in which features are selected that would otherwise be excluded with an accuracy-based measure, which is another noteworthy finding.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.10.010
  • Structural test data generation using a memetic ant colony optimization
           based on evolution strategies
    • Authors: Hossein Sharifipour; Mojtaba Shakeri; Hassan Haghighi
      Abstract: Publication date: Available online 20 December 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Hossein Sharifipour, Mojtaba Shakeri, Hassan Haghighi
      Test data generation is one of the key activities that has a significant impact on the efficiency and effectiveness of software testing. Since manual test data generation is quite inefficient and even impractical, automated test data generation has been realized to produce an appropriate subset of input data to carry out effective software testing in reasonable times. This paper presents a memetic ant colony optimization (ACO) algorithm for structural test data generation. The proposed approach incorporates (1+1)-evolution strategies (ES) to improve the search functionality of ants in local moves and enhance search exploitation. Moreover, we have introduced a novel definition of the pheromone functionality in the way that it discourages ants from choosing mostly covered paths of the program to reinforce search exploration. Given that branch coverage is considered as the coverage criterion, two fitness functions are used accordingly for our proposed algorithm. The first fitness function is a Boolean function which is particularly defined to maximize branch coverage. It outputs one if a given solution is successful in traversing at least a yet uncovered branch; otherwise, it returns zero. The second fitness function is formulated according to the complexity of branches covered. The value of the second fitness function is not taken into account for solutions whose Boolean function value equals one. For these solutions, the decision-making process of ants is merely carried out based on the first fitness function. The experimental results indicate the superiority of our memetic ACO algorithm relative to existing test data generation techniques in terms of both branch coverage and convergence speed.

      PubDate: 2017-12-27T13:32:09Z
      DOI: 10.1016/j.swevo.2017.12.009
  • Rank Fusion and Semantic Genetic Notion Based Automatic Query Expansion
    • Authors: Jagendra Singh; Aditi Sharan
      Abstract: Publication date: Available online 13 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jagendra Singh, Aditi Sharan
      Query expansion term selection methods are really very important for improving the accuracy and efficiency of pseudo-relevance feedback based automatic query expansion for information retrieval system by removing irrelevant and redundant terms from the top retrieved feedback documents corpus with respect to user query. Individual query expansion term selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual query expansion term selection method that would outperform other individual query expansion term selection methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual query expansion term selection methods. Second, we propose a model for combining multiple query expansion term selection methods by using rank combination approach, called multiple ranks combination based query expansion. Third, semantic filtering is used to filter semantically irrelevant term obtained after combining multiple query expansion term selection methods, called ranks combination and semantic filtering based query expansion. Fourth, the genetic algorithm is used to make an optimal combination of query terms and candidate term obtained after rank combination and semantic filtering approach, called semantic genetic filtering and rank combination based query expansion. Our experimental results demonstrated that our proposed approaches achieved significant improvement over each individual query expansion term selection method and related state-of-the-art approaches.

      PubDate: 2017-09-16T13:42:43Z
      DOI: 10.1016/j.swevo.2017.09.007
  • Image contrast enhancement using an artificial bee colony algorithm
    • Authors: Jia Chen; Weiyu Yu; Jing Tian; Li Chen; Zhili Zhou
      Abstract: Publication date: Available online 9 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jia Chen, Weiyu Yu, Jing Tian, Li Chen, Zhili Zhou
      The objective of image contrast enhancement is to improve the contrast level of images, which are degraded during image acquisition. Image contrast enhancement is considered as an optimization problem in this paper and the artificial bee colony (ABC) algorithm is utilized to find the optimal solution for this optimization problem. The contribution of the proposed approach is two-fold. First, in view of that the fitness function is indispensable to evaluate the quality of the enhanced image, a new objective fitness function is proposed in this paper. Second, the image transformation function is critical to generate new pixel intensities for the enhanced image from the original input image; more importantly, it guides the searching movements of the artificial bees. For that, a parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm. This is in contrast to that the whole space of image intensity levels is used in the conventional ABC-based image enhancement approaches. Extensive experiments are conducted to demonstrate that the proposed approach outperforms conventional image contrast enhancement approaches to achieve both better visual image quality and higher objective performance measures.

      PubDate: 2017-09-10T09:28:23Z
      DOI: 10.1016/j.swevo.2017.09.002
  • Binary Grey Wolf Optimizer for large scale unit commitment problem
    • Authors: Lokesh Kumar Panwar; Srikanth Reddy K; Ashu Verma; B.K. Panigrahi; Rajesh Kumar
      Abstract: Publication date: Available online 24 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Lokesh Kumar Panwar, Srikanth Reddy K, Ashu Verma, B.K. Panigrahi, Rajesh Kumar
      The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.08.002
  • Computing budget allocation in multi-objective evolutionary algorithms for
           stochastic problems
    • Authors: Mengmei Liu; Aaron M. Cramer
      Abstract: Publication date: Available online 24 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Mengmei Liu, Aaron M. Cramer
      Multi-objective stochastic problems are important problems in practice and are often solved through multi-objective evolutionary algorithms. Researchers have developed different noise handling techniques to improve the efficiency and accuracy of such algorithms, primarily by integrating these methods into the evaluation or environmental selection steps of the algorithms. In this work, a combination of studies that compare integration of different computing budget allocation methods into either the evaluation or the environmental selection steps are conducted. These comparisons are performed on stochastic problems derived from benchmark multi-objective optimization problems and consider varying levels of noise. The algorithms are compared in terms of both proximity to and coverage of the true Pareto-optimal front and sufficient studies are performed to allow statistically significant conclusions to be drawn. It is shown that integrating computing budget allocation methods into the environmental selection step is better than integration within the evaluation step.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.08.004
  • A distributed evolutionary multivariate discretizer for Big Data
           processing on Apache Spark
    • Authors: S. Ramírez-Gallego; S. García; J.M. Benítez; F. Herrera
      Abstract: Publication date: Available online 23 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera
      Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Discretization (as part of data reduction) is presented as a real solution to reduce this complexity. However, standard discretizers are not designed to perform well with such amounts of data. This paper proposes a distributed discretization algorithm for Big Data analytics based on evolutionary optimization. After comparing with a distributed discretizer based on the Minimum Description Length Principle, we have found that our solution yields more accurate and simpler solutions in reasonable time.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.08.005
  • LONSA: A labeling-oriented non-dominated sorting algorithm for
           evolutionary many-objective optimization
    • Authors: R.F. Alexandre; C.H.N.R. Barbosa; J.A. Vasconcelos
      Abstract: Publication date: Available online 18 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): R.F. Alexandre, C.H.N.R. Barbosa, J.A. Vasconcelos
      Multiobjective algorithms are powerful in tackling complex optmization problems mathematically represented by two or more conflicting objective functions and their constraints. Sorting a set of current solutions across non-dominated fronts is the key step for the searching process to finally identify which ones are the best solutions. To perform that step, a high computational effort is demanded, especially if the size of the solution set is huge or the mathematical model corresponds to a many-objective problem. In order to overcome this, a new labeling-oriented algorithm is proposed in this paper to speed up the solution-to-front assignment by avoiding usual dominance tests. Along with this algorithm, called Labeling-Oriented Non-dominated Sorting Algorithm (LONSA), the associated methodology is carefully detailed to clearly explain how the classification of the solution set is successfully achieved. This work presents a comparison between LONSA and other well-known algorithms usually found in the literature. The simulation results have shown a better performance of the proposed algorithm against nine chosen strategies in terms of computational time as well as number of comparisons.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.08.003
  • Improved gene expression programming to solve the inverse problem for
           ordinary differential equations
    • Authors: Kangshun Li; Yan Chen; Wei Li; Jun He; Yu Xue
      Abstract: Publication date: Available online 12 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Kangshun Li, Yan Chen, Wei Li, Jun He, Yu Xue
      Many complex systems in the real world evolve with time. These dynamic systems are often modeled by ordinary differential equations in mathematics. The inverse problem of ordinary differential equations is to convert the observed data of a physical system into a mathematical model in terms of ordinary differential equations. Then the model may be used to predict the future behavior of the physical system being modeled. Genetic programming has been taken as a solver of this inverse problem. Similar to genetic programming, gene expression programming could do the same job since it has a similar ability of establishing the model of ordinary differential systems. Nevertheless, such research is seldom studied before. This paper is one of the first attempts to apply gene expression programming for solving the inverse problem of ordinary differential equations. Based on a statistic observation of traditional gene expression programming, an improvement is made in our algorithm, that is, genetic operators should act more often on the dominant part of genes than on the recessive part. This may help maintain population diversity and also speed up the convergence of the algorithm. Experiments show that this improved algorithm performs much better than genetic programming and traditional gene expression programming in terms of running time and prediction precision.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.07.005
  • An investigation into many-objective optimization on combinatorial
           problems: analyzing the pickup and delivery problem
    • Authors: Abel García-Nájera; Antonio López-Jaimes
      Abstract: Publication date: Available online 4 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Abel García-Nájera, Antonio López-Jaimes
      Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several proposals have appeared. An important result states that the problem does not necessarily becomes more difficult while more objectives are considered. Nevertheless, combinatorial problems have not received an appropriate attention, making this an open research area. This investigation takes this subject on by studying a many-objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists of finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, in many applications, some other objectives emerge, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of: (i) the performance of four representative multi-objective evolutionary algorithms on PDP varying the number of objectives, (ii) the properties of the many-objective PDP regarding scalability, i.e. the conflict between each pair of objectives and the proportion of non-dominated solutions as the number of objectives is varied, and finally (iii) the change of PDP's difficulty when the number of objectives is increased. Results show that the regarded objectives are actually in conflict and that the problem is more difficult to solve while more objectives are considered.

      PubDate: 2017-08-04T18:36:10Z
      DOI: 10.1016/j.swevo.2017.08.001
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