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Journal Cover Swarm and Evolutionary Computation
  [SJR: 2.167]   [H-I: 22]   [3 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [3042 journals]
  • 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
      Abstract: Publication date: Available online 23 June 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-06-23T08:00:18Z
      DOI: 10.1016/j.swevo.2017.06.002
       
  • Learning-driven many-objective evolutionary algorithms for
           satellite-ground time synchronization task planning problem
    • Authors: Zhongshan Zhang; Yuning Chen; Lei He; Lining Xing; Yuejin Tan
      Abstract: Publication date: Available online 16 June 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Zhongshan Zhang, Yuning Chen, Lei He, Lining Xing, Yuejin Tan
      The satellite-ground time synchronization task planning problem (SGTSTP) is a complex many-objective ground station scheduling problem in navigation systems. In this paper, we first provide a mathematical formulation of the SGTSTP. To solve this complex problem, we propose a decomposition and integration method, based on which the task planning problem is transformed into a multi-period 0-1 programming problem. Given that many-objective evolutionary algorithms (MaOEAs) are time-consuming and the SGTSTP focuses on optimality rather than solution diversity, we integrate learning concepts with conventional MaOEAs to form a learning-driven MaOEA (LD-MaOEA). The LD-MaOEA generates a partial solution with genetic operators, and completes the partial solution using the dynamic learning-based roll planning algorithm (DLRPA). Additionally, we design two learning strategies in the DLRPA. Finally, we design two sets of instances. The computational results demonstrate that LD-MaOEAs have obvious performance promotion in terms of the solving effect and efficiency compared with MaOEAs. Furthermore, we propose a comprehensive metric to help to identify a preferable solution from the solution set obtained using MaOEAs.

      PubDate: 2017-06-17T07:51:46Z
      DOI: 10.1016/j.swevo.2017.05.011
       
  • A knowledge-guided multi-objective fruit fly optimization algorithm for
           the multi-skill resource constrained project scheduling problem
    • Authors: Ling Wang; Xiao-long Zheng
      Abstract: Publication date: Available online 8 June 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-06-13T07:37:16Z
      DOI: 10.1016/j.swevo.2017.06.001
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2017
      Source:Swarm and Evolutionary Computation, Volume 34


      PubDate: 2017-06-03T06:57:45Z
       
  • Sizing and topology optimization of truss structures using genetic
           programming
    • Authors: Hirad Assimi; Ali Jamali; Nader Nariman-zadeh
      Abstract: Publication date: Available online 31 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Hirad Assimi, Ali Jamali, Nader Nariman-zadeh
      This paper presents a genetic programming approach for simultaneous optimization of sizing and topology of truss structures. It aims to find the optimal cross-sectional areas and connectivities of the joints to achieve minimum weight in the search space. The structural optimization problem is subjected to kinematic stability, maximum allowable stress and deflection. This approach uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the optimum solution. This method has the capability to identify redundant truss elements and joints in the design space. The obtained results are compared with existing popular and competent techniques in literature and its competence as a tool in the optimization problem are demonstrated in solving some benchmark examples, the proposed approach provided lighter truss structures than the available solutions reported in the literature.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.009
       
  • Multimodal Optimization Problem in Contamination Source Determination of
           Water Supply Networks
    • Authors: Xuesong Yan; Jing Zhao; Chengyu Hu; Deze Zeng
      Abstract: Publication date: Available online 26 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Xuesong Yan, Jing Zhao, Chengyu Hu, Deze Zeng
      It makes great economic losses and bad social influence for our country about some accidental drinking water contamination and vicious attacks to water distribution networks. In terms of solving the problems of drinking water contamination caused by accidental contaminant event in water supply network, used the techniques just like sensor network which could determinate the source location to isolate the contaminated area and minimized its hazards. Previous studies have shown that the contamination source determination problem model can be utilized to convert the contamination source determination problem to an unimodal function optimization problem. However, we notice that it is a multimodal function optimization problem in essence and the number of its solution has non-uniqueness feature. In this paper, we first modified the problem model with formulate the threshold value based on the previous works and proposed the niching genetic algorithm calculate multiple contamination sources, and provide the possibility for screening the true contamination source. Furthermore, this paper applies different distribution networks verify the validity after the threshold formulation as well as the effectiveness of algorithm from various aspects.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.010
       
  • Evolutionary heterogeneous clustering for rating prediction based on user
           collaborative filtering
    • Authors: Jianrui Chen; Uliji; Hua Wang; Zaizai Yan
      Abstract: Publication date: Available online 25 May 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.008
       
  • Multiobjective evolutionary algorithm based on vector angle neighborhood
    • Authors: Roman Denysiuk; António Gaspar-Cunha
      Abstract: Publication date: Available online 19 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Roman Denysiuk, António Gaspar-Cunha
      Selection is a major driving force behind evolution and is a key feature of multiobjective evolutionary algorithms. Selection aims at promoting the survival and reproduction of individuals that are most fitted to a given environment. In the presence of multiple objectives, major challenges faced by this operator come from the need to address both the population convergence and diversity, which are conflicting to a certain extent. This paper proposes a new selection scheme for evolutionary multiobjective optimization. Its distinctive feature is a similarity measure for estimating the population diversity, which is based on the angle between the objective vectors. The smaller the angle, the more similar individuals. The concept of similarity is exploited during the mating by defining the neighborhood and the replacement by determining the most crowded region where the worst individual is identified. The latter is performed on the basis of a convergence measure that plays a major role in guiding the population towards the Pareto optimal front. The proposed algorithm is intended to exploit strengths of decomposition-based approaches in promoting diversity among the population while reducing the user's burden of specifying weight vectors before the search. The proposed approach is validated by computational experiments with state-of-the-art algorithms on problems with different characteristics. The obtained results indicate a highly competitive performance of the proposed approach. Significant advantages are revealed when dealing with problems posing substantial difficulties in keeping diversity, including many-objective problems. The relevance of the suggested similarity and convergence measures are shown. The validity of the approach is also demonstrated on engineering problems.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.005
       
  • Modified cuckoo search algorithm for multiobjective short-term
           hydrothermal scheduling
    • Authors: Thang Trung Nguyen; Dieu Ngoc Vo
      Abstract: Publication date: Available online 19 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Thang Trung Nguyen, Dieu Ngoc Vo
      This paper proposes a modified cuckoo search algorithm (MCSA) for solving multi-objective short-term fixed head hydrothermal scheduling (HTS) problem. The main objective of the multiobjective HTS problem is to minimize both total power generation cost and emission of thermal generators over a scheduling period while satisfying power balance, hydraulic, and generator operating limit constraints. The proposed MCSA method is developed for the problem based on improvements from the conventional CSA method which is a new metaheuristic algorithm inspired from the behavior of some cuckoo species laying their egg into the nest of other species to improve the optimal solution and speed up the computational process. In the MCSA method, the nests are evaluated and classified into two groups including the top group with better quality eggs and the abandoned group with worse quality eggs. Two effective strategies via Lévy flights for producing new solutions are applied to the abandoned and top groups. To validate the efficiency of the MCSA method, several test systems have been tested and the result comparisons from the test systems have indicated that the proposed method can obtain higher quality solution and shorter computational time than many other methods. Therefore, the proposed MCSA method can be new efficient method for solving multiobjective short-term fixed-head HTS problems.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.006
       
  • An effective invasive weed optimization algorithm for scheduling
           semiconductor final testing problem
    • Authors: Hong-Yan Sang; Pei-Yong Duan; Jun-Qing Li
      Abstract: Publication date: Available online 19 May 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.007
       
  • Reply to comments on “Evolutionary and GPU Computing for Topology
           Optimization of Structures”
    • Authors: Deepak Sharma
      Abstract: Publication date: Available online 17 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Deepak Sharma
      This short communication is in reply to the comments made on the published article on “Evolutionary and GPU Computing for Topology Optimization of Structures”.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.004
       
  • Jaya, harmony search and water cycle algorithms for solving large-scale
           real-life urban traffic light scheduling problem
    • Authors: Kaizhou Gao; Yicheng Zhang; Ali Sadollah; Antonios Lentzakis; Rong Su
      Abstract: Publication date: Available online 17 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Kaizhou Gao, Yicheng Zhang, Ali Sadollah, Antonios Lentzakis, Rong Su
      This paper studies a large-scale urban traffic light scheduling problem (LUTLSP). A centralized model is developed to describe the LUTLSP, where each outgoing flow rate is described as a nonlinear mixed logical switching function over the source link’s density, the destination link’s density and capacity, and the driver’s potential psychological response to the past traffic light signals. The objective is to minimize the total network-wise delay time of all vehicles in a time window. Three metaheuristic optimization algorithms, named as Jaya algorithm, harmony search (HS) and water cycle algorithm (WCA) are implemented to solve the LUTLSP. Since we adopt a discrete-time formulation of LUTLSP, we firstly develop a discrete version of Jaya and WCA. Secondly, some improvement strategies are proposed to speed up the convergence of applied optimizers. Thirdly, a feature based search operator is utilized to improve the search performance of reported optimization methods. Finally, experiments are carried out based on the real traffic data in Singapore. The HS, WCA, Jaya, and their variants are evaluated by solving 11 cases of traffic networks. The comparisons and discussions verify that the considered metaheuristic optimization methods can effectively solve the LUTLSP considerably surpassing the existing traffic light control strategy.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.002
       
  • Global-best brain storm optimization algorithm
    • Authors: Mohammed El-Abd
      Abstract: Publication date: Available online 9 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Mohammed El-Abd
      Brain storm optimization (BSO) is a population-based metaheuristic algorithm that was recently developed to mimic the brainstorming process in humans. It has been successfully applied to many real-world engineering applications involving non-linear continuous optimization. In this work, we propose improving the performance of BSO by introducing a global-best version combined with per-variable updates and fitness-based grouping. In addition, the proposed algorithm incorporates a re-initialization scheme that is triggered by the current state of the population. The introduced Global-best BSO (GBSO) is compared against other BSO variants on a wide range of benchmark functions. Comparisons are based on final solutions and convergence characteristics. In addition, GBSO is compared against global-best versions of other meta-heuristics on recent benchmark libraries. Results prove that the proposed GBSO outperform previous BSO variants on a wide range of classical functions and different problem sizes. Moreover, GBSO outperforms other global-best meta-heuristic algorithms on the well-known CEC05 and CEC14 benchmarks.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.001
       
  • A backtracking search hyper-heuristic for the distributed assembly
           flow-shop scheduling problem
    • Authors: Jian Lin; Zhou-Jing Wang; Xiaodong Li
      Abstract: Publication date: Available online 29 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jian Lin, Zhou-Jing Wang, Xiaodong Li
      Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as an important class of problems in modern supply chains and manufacturing systems. In this paper, a backtracking search hyper-heuristic (BS-HH) algorithm is proposed to solve the DAPFSP. In the BS-HH scheme, ten simple and effective heuristic rules are designed to construct a set of low-level heuristics (LLHs), and the backtracking search algorithm is employed as the high-level strategy to manipulate the LLHs to operate on the solution space. Additionally, an efficient solution encoding and decoding scheme is proposed to generate a feasible schedule. The effectiveness of the BS-HH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.04.007
       
  • Differential evolution algorithm-based range image registration for
           free-form surface parts quality inspection
    • Authors: Taifeng Li; Quanke Pan; Liang Gao; Peigen Li
      Abstract: Publication date: Available online 26 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Taifeng Li, Quanke Pan, Liang Gao, Peigen Li
      Increasing demands on precision manufacturing of complex free-form surface parts have been observed in the past several years. Although some advanced techniques have been employed to solve the design and machining problems for such parts, quality inspection remains a difficult problem. Registration is a crucial issue in surface inspection; it is used to transform the design model and measurement model into a common coordinate system. The comparison results are then outputted in a report and displayed visually by color gradients. This paper presents a design model-based inspection method with range image registration, in which the measurement model is represented by a series of 3D discrete points. In the model preprocessing, the directed Hausdorff distance (DHD) method is employed for point cloud simplification, and a novel point descriptor is designed to evaluate the property of each point. Subsequently, a differential evolution (DE) algorithm-based optimizer is proposed for error evaluation. Combined with the properties of 3D points, the optimizer can measure the similarity between the design model and the measurement model with a recursive process. The proposed algorithms have been implemented and tested with several sets of simulated and real data. The experiment results illustrate that they are effective and efficient for free-form surface part quality inspection.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.04.006
       
  • Micro-time variant multi-objective particle swarm optimization
           (micro-TVMOPSO) of a solar thermal combisystem
    • Authors: Anthony Rey; Radu Zmeureanu
      Abstract: Publication date: Available online 21 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Anthony Rey, Radu Zmeureanu
      Multi-objective optimization (MOO) algorithms usually require a high number of objective function evaluations to approximate the Pareto-optimal solutions, which can be time-consuming in many engineering applications. To overcome this issue, MOO algorithms using a small population, often referred to as micro-MOO algorithms, aim at approximating the true Pareto front using a smaller number of objective function evaluations. Such algorithms are not common in the evolutionary algorithms literature and their usage in building engineering is seldom. This paper proposes a micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO), which is a revised version of the micro-MOPSO algorithm. First, the proposed algorithm is applied along with eight other MOO algorithms to 24 benchmark problems, and their performance is compared by using two metrics. Although the proposed micro-TVMOPSO algorithm faced difficulties in solving some complicated Pareto fronts, it outperformed the eight optimization algorithms on 10 of the 24 selected benchmark problems. After the comparison of proposed micro-TVMOPSO with several MOO algorithms for different benchmark problems, the micro-TVMOPSO is applied to a case study in engineering: the design optimization of a residential solar thermal combisystem using two conflicting objective functions, the life cycle cost (LCC) and energy use (LCE). Different patterns of variation of the decision variables were observed from the non-dominated solutions found by micro-TVMOPSO, which would have not been possible to notice by performing a single-objective optimization. For instance, the increase of number of solar collectors from 1 to 10 had the impact of increasing the LCC by 84% and decreasing the LCE by 63%. The results indicated that the number of solar thermal collectors is the variable having the most effect on both LCC and LCE. When the number of collectors increases, more energy is harvested, and larger tanks and less auxiliary electric power are needed.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.04.005
       
  • Evolutionary multi-objective fault diagnosis of power transformers
    • Authors: Abdolrahman Peimankar; Stephen John Weddell; Thahirah Jalal; Andrew Craig Lapthorn
      Abstract: Publication date: Available online 17 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Abdolrahman Peimankar, Stephen John Weddell, Thahirah Jalal, Andrew Craig Lapthorn
      This paper introduces a two step algorithm for fault diagnosis of power transformers (2-ADOPT) using a binary version of the multi-objective particle swarm optimization (MOPSO) algorithm. Feature subset selection and ensemble classifier selection are implemented to improve the diagnosing accuracy for dissolved gas analysis (DGA) of power transformers. First, the proposed method selects the most effective features in a multi objective framework and the optimum number of features, simultaneously, which are used as inputs to train classifiers in the next step. The input features are composed of DGA performed on the oil of power transformers along with the various ratios of these gases. In the second step, the most accurate and diverse classifiers are selected to create a classifier ensemble. Finally, the outputs of selected classifiers are combined using the Dempster-Shafer combination rule in order to determine the actual faults of power transformers. In addition, the obtained results of the proposed method are compared to three other scenarios: 1) multi-objective ensemble classifier selection without any feature selection step which takes all the features to train classifiers and then applies MOPSO algorithm to find the best ensemble of classifiers, 2) a well-known classifier ensemble technique called random forests, and 3) another powerful decision tree ensemble which is called oblique random forests. The comparison results were favourable to the proposed method and showed the high reliability of this method for power transformers fault classification.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.03.005
       
  • Multi-objective genetic algorithm with variable neighbourhood search for
           the electoral redistricting problem
    • Authors: Leonardo Vanneschi; Roberto Henriques; Mauro Castelli
      Abstract: Publication date: Available online 13 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Leonardo Vanneschi, Roberto Henriques, Mauro Castelli
      In a political redistricting problem, the aim is to partition a territory into electoral districts or clusters, subject to some constraints. The most common of these constraints include contiguity, population equality, and compactness. We propose an algorithm to address this problem based on multi-objective optimization. The hybrid algorithm we propose combines the use of the well-known Pareto-based NSGA-II technique with a novel variable neighbourhood search strategy. A new ad-hoc initialization method is also proposed. Finally, new specific genetic operators that ensure the compliance of the contiguity constraint are introduced. The experimental results we present, which are performed considering five US states, clearly show the appropriateness of the proposed hybrid algorithm for the redistricting problem. We give evidence of the fact that our method produces better and more reliable solutions with respect to those returned by the state-of-the-art methods.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.04.003
       
  • Voxelisation in the 3-D Fly Algorithm for PET
    • Authors: Zainab Ali Abbood; Julien Lavauzelle; Évelyne Lutton; Jean-Marie Rocchisani; Jean Louchet; Franck P. Vidal
      Abstract: Publication date: Available online 13 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Zainab Ali Abbood, Julien Lavauzelle, Évelyne Lutton, Jean-Marie Rocchisani, Jean Louchet, Franck P. Vidal
      The Fly Algorithm was initially developed for 3-D robot vision applications. It consists in solving the inverse problem of shape reconstruction from projections by evolving a population of 3-D points in space (the ‘flies’), using an evolutionary optimisation strategy. Here, in its version dedicated to tomographic reconstruction in medical imaging, the flies are mimicking radioactive photon sources. Evolution is controlled using a fitness function based on the discrepancy of the projections simulated by the flies with the actual pattern received by the sensors. The reconstructed radioactive concentration is derived from the population of flies, i.e. a collection of points in the 3-D Euclidean space, after convergence. ‘Good’ flies were previously binned into voxels. In this paper, we study which flies to include in the final solution and how this information can be sampled to provide more accurate datasets in a reduced computation time. We investigate the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. The spread of each Gaussian kernel is modulated in function of the corresponding fly fitness. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test-cases, data fidelity increases by more than 10% when density fields are used instead of binning. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (filtered back-projection and ordered subset expectation-maximisation).
      Graphical abstract image

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.04.001
       
  • A hybrid algorithm using ant and bee colony optimization for feature
           selection and classification (AC-ABC Hybrid)
    • Authors: P. Shunmugapriya; S. Kanmani
      Abstract: Publication date: Available online 11 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): P. Shunmugapriya, S. Kanmani
      Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) are famous meta-heuristic search algorithms used in solving numerous combinatorial optimization problems. Feature Selection (FS) helps to speed up the process of classification by extracting the relevant and useful information from the dataset. FS is seen as an optimization problem because selecting the appropriate feature subset is very important. This paper proposes a novel Swarm based hybrid algorithm AC-ABC Hybrid, which combines the characteristics of Ant Colony and Artificial Bee Colony (ABC) algorithms to optimize feature selection. By hybridizing, we try to eliminate stagnation behavior of the ants and time consuming global search for initial solutions by the employed bees. In the proposed algorithm, Ants use exploitation by the Bees to determine the best Ant and best feature subset; Bees adapt the feature subsets generated by the Ants as their food sources. Thirteen UCI (University of California, Irvine) benchmark datasets have been used for the evaluation of the proposed algorithm. Experimental results show the promising behavior of the proposed method in increasing the classification accuracies and optimal selection of features.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.04.002
       
  • Estimation of transformer parameters from nameplate data by imperialist
           competitive and gravitational search algorithms
    • Authors: H.A. Illias; K.J. Mou; A.H.A. Bakar
      Abstract: Publication date: Available online 7 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): H.A. Illias, K.J. Mou, A.H.A. Bakar
      Accurate determination of parameters in power transformer equivalent circuit is important because it can influence the simulation results of condition monitoring on power transformers, such as analysis of frequency-response. This is due to inaccurate simulation results will yield incorrect interpretation of the power transformer condition through its equivalent circuit. Works on development of transformer models have been widely developed since the past for transient and steady-state analyses. Estimating parameters of a transformer using nameplate data without performing a single experiment has been developed in the past. However, the average error between the actual and estimated parameter values in the past work using Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) is considerably large. This signifies that there is a room for improvement by using other optimisation techniques, such as state of the art methods which include Heterogeneous Comprehensive Learning PSO (HCLPSO), LSHADE-EpSin, Imperialist Competitive Algorithm (ICA), Gravitational Search Algorithm (GSA) and others. Since ICA and GSA have advantages over GA and PSO, in this work, estimation of transformer parameters from its nameplate data was proposed using ICA and GSA. The results obtained using ICA and GSA was compared to those using GA and PSO to determine the parameters of transformer equivalent circuit. The results show that GSA performs the best as it gives the lowest average error compared to PSO, GA and ICA. Therefore, the proposed technique using GSA and ICA can give a better accuracy than PSO and GA in estimating the parameters of power transformers. The proposed method can also be applied to estimate parameters of three-phase transformers from their nameplate data without disconnecting them from the grid for testing.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.03.003
       
  • Intelligent dynamic spectrum access using hybrid genetic operators
    • Authors: Md. Jahidul Islam; Md. Monirul Islam; A.B.M. Alim Al Islam
      Abstract: Publication date: Available online 5 April 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Md. Jahidul Islam, Md. Monirul Islam, A.B.M. Alim Al Islam
      This paper presents a novel hybrid dynamic spectrum access approach, combining classical and stochastic flavors being augmented with new genetic operators, for multi-channel single-radio cognitive radio networks. Existing classical and stochastic approaches exhibit different advantages and disadvantages depending on network architecture. Our proposed approach exploits a delicate balance between the two different approaches for extracting advantages from both of them while limiting their disadvantages. Additionally, in our proposed approach, we boost up extent of the exploitation through designing new genetic operators. Furthermore, we provide a thorough performance evaluation of our approach using a widely used discrete event simulator called ns-2. Here, we also simulate several existing approaches that are based on graph theory, game theory, heuristic, genetic algorithm, agent-based learning, and online learning. Simulation results demonstrate significant performance improvement using our proposed intelligent dynamic spectrum access approach over these state-of-the-art ones based on various standard metrics.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.03.004
       
  • A niche GSA method with nearest neighbor scheme for multimodal
           optimization
    • Authors: Pourya Haghbayan; Hossein Nezamabadi-pour; Shima Kamyab
      Abstract: Publication date: Available online 28 March 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Pourya Haghbayan, Hossein Nezamabadi-pour, Shima Kamyab
      In this paper, a new niching method based on Gravitational Search Algorithm (GSA) is proposed in which species are formed within the population (swarm) based on a nearest neighbor (NN) scheme. Also, we suggest a scheme to detect the niches inside the population by using the hill valley algorithm without the need of a pairwise comparison between any pair of solutions inside the population. In order to improve the exploitation capability of the proposed niching method, the formed species are balanced such that they are forced to have almost equal number of members. This mechanism enables the species to explore more optima via diversity conservation in the swarm. Experimental results of using several multimodal benchmark functions confirm the effectiveness of the proposed niching scheme compared to well-known existing niching methods.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.03.002
       
  • Large-scale cooperative co-evolution using niching-based multi-modal
           optimization and adaptive fast clustering
    • Authors: Xingguang Peng; Yapei Wu
      Abstract: Publication date: Available online 16 March 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Xingguang Peng, Yapei Wu
      The divide-and-conquer problem-solving manner endows the cooperative co-evolutionary (CC) algorithms with a promising perspective for the large-scale global optimization (LSGO). However, by dividing a problem into several sub-components, the co-evolutionary information can be lost to some extent, which may lead to sub-optimization. Thus, information compensation is a crucial aspect of the design of efficient CC algorithms. This paper aims to scale up the information compensation for the LSGO. First, a niching-based multi-modal optimization procedure was introduced into the canonical CC framework to provide more informative collaborators for the sub-components. The information compensation was achieved with these informative collaborators, which is positive for the LSGO. Second, a simple but efficient clustering method was extended to run without manually setting the cut-off distance and identifying clusters. This clustering method, together with a simple scheme, was incorporated to prevent the combinational explosion when mixing the collaborator with a given individual to conduct the fitness evaluation. The effectiveness and superiority of the proposed algorithm were justified by a comprehensive experimental study that compared 8 state-of-the-art large-scale CC algorithms and 8 metaheuristic algorithms on two 1000-dimensional benchmark suites with 20 and 15 test functions, respectively.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.03.001
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: April 2017
      Source:Swarm and Evolutionary Computation, Volume 33


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

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

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

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

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

      PubDate: 2017-02-23T00:23:13Z
      DOI: 10.1016/j.swevo.2017.02.004
       
  • Particle Swarm Clustering Fitness Evaluation with Computational Centroids
    • Authors: Jenni Raitoharju; Kaveh Samiee; Serkan Kiranyaz; Moncef Gabbouj
      Abstract: Publication date: Available online 9 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jenni Raitoharju, Kaveh Samiee, Serkan Kiranyaz, Moncef Gabbouj
      In this paper, we propose a new way to carry out fitness evaluation in dynamic Particle Swarm Clustering (PSC) with centroid-based encoding. Generally, the PSC fitness function is selected among the clustering validity indices and most of them directly depend on the cluster centroids. In the traditional fitness evaluation approach, the cluster centroids are replaced by the centroids proposed by a particle position. We propose to first compute the centroids of the corresponding clusters and then use these computational centroids in fitness evaluation. The proposed way is called Fitness Evaluation with Computational Centroids (FECC). We conducted an extensive set of comparative evaluations and the results show that FECC leads to a clear improvement in clustering results compared to the traditional fitness evaluation approach with most of the fitness functions considered in this study. The proposed approach was found especially beneficial when underclustering is a problem. Furthermore, we evaluated 31 fitness functions based on 17 clustering validity indices using two PSC methods over a large number of synthetic and real data sets with varying properties. We used three different performance criteria to evaluate the clustering quality and found out that the top three fitness functions are Xu index, WB index, and Dunn variant DU 23 applied using FECC. These fitness functions were consistently performing well for both PSC methods, for all data distributions, and according to all performance criteria. In all test cases, they were clearly among the better half of the fitness functions and, in the majority of the cases, they were among the top 4 functions. Further guidance for improved fitness function selection in different situations is provided in the paper.

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

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

      PubDate: 2017-01-29T08:55:12Z
      DOI: 10.1016/j.swevo.2017.01.002
       
  • A survey of swarm intelligence for dynamic optimization: algorithms and
           applications
    • Authors: Michalis Mavrovouniotis; Changhe Li; Shengxiang Yang
      Abstract: Publication date: Available online 11 January 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Michalis Mavrovouniotis, Changhe Li, Shengxiang Yang
      Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constraint, multi-objective and classification, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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

      PubDate: 2017-01-05T16:38:06Z
      DOI: 10.1016/j.swevo.2016.12.004
       
 
 
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