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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  [3043 journals]
  • Intelligent dynamic spectrum access using hybrid genetic operators
    • Authors: Md. Jahidul Islam; Md. Monirul Islam; A.B.M. Alim Al Islam
      Pages: 1 - 17
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.03.004
      Issue No: Vol. 36 (2017)
  • 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
      Pages: 18 - 26
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.03.003
      Issue No: Vol. 36 (2017)
  • A hybrid algorithm using ant and bee colony optimization for feature
           selection and classification (AC-ABC Hybrid)
    • Authors: P. Shunmugapriya; S. Kanmani
      Pages: 27 - 36
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.04.002
      Issue No: Vol. 36 (2017)
  • Multi-objective genetic algorithm with variable neighbourhood search for
           the electoral redistricting problem
    • Authors: Leonardo Vanneschi; Roberto Henriques; Mauro Castelli
      Pages: 37 - 51
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.04.003
      Issue No: Vol. 36 (2017)
  • Evolutionary multi-objective fault diagnosis of power transformers
    • Authors: Abdolrahman Peimankar; Stephen John Weddell; Thahirah Jalal; Andrew Craig Lapthorn
      Pages: 62 - 75
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.03.005
      Issue No: Vol. 36 (2017)
  • Micro-time variant multi-objective particle swarm optimization
           (micro-TVMOPSO) of a solar thermal combisystem
    • Authors: Anthony Rey; Radu Zmeureanu
      Pages: 76 - 90
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.04.005
      Issue No: Vol. 36 (2017)
  • 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
      Pages: 91 - 105
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.04.001
      Issue No: Vol. 36 (2017)
  • Differential evolution algorithm-based range image registration for
           free-form surface parts quality inspection
    • Authors: Taifeng Li; Quanke Pan; Liang Gao; Peigen Li
      Pages: 106 - 123
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.04.006
      Issue No: Vol. 36 (2017)
  • A backtracking search hyper-heuristic for the distributed assembly
           flow-shop scheduling problem
    • Authors: Jian Lin; Zhou-Jing Wang; Xiaodong Li
      Pages: 124 - 135
      Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36
      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: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.04.007
      Issue No: Vol. 36 (2017)
  • Evolutionary and GPU computing for topology optimization of structures
    • Authors: Laxman Ram; Deepak Sharma
      Pages: 1 - 13
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      Author(s): Laxman Ram, Deepak Sharma
      Although structural topology optimization, as a discrete optimization problem, has been successfully solved several times in the literature using evolutionary algorithms (EAs), the two key difficulties lie in generating geometrically feasible structures and handling a high computation time. These two challenges are addressed in this paper by adopting triangular representation for two-dimensional continuum structures, related crossover and mutation operators, and by performing computations in parallel on the graphics processing unit (GPU). Two case studies are solved on the GPU that show 5× of speedup over CPU implementation. The parametric study on the population size of EA shows that the approximate Pareto-optimal solutions can be evolved using a small population with the proposed EA operators.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2016.08.004
      Issue No: Vol. 35 (2017)
  • A feasible method for controlled intentional islanding in microgrids based
           on PSO algorithm
    • Authors: M.H. Oboudi; R. Hooshmand; A. Karamad
      Pages: 14 - 25
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      Author(s): M.H. Oboudi, R. Hooshmand, A. Karamad
      Intentional islanding is a suitable approach to increase the system reliability in situations where the electrical connection between the microgrid and the upstream network is lost. In this paper, an innovated two-stage method for the intentional islanding process in microgrids is proposed. The important and practical issues such as the load controllability; load priorities; voltage and the line capacity constraints; reduction of problem solution space and ability to make larger islands are taken into account. In the first stage, the problem is relaxed by considering the problem as an optimization problem known as Tree Knapsack Problem (TKP) solved by Particle Swarm Optimization (PSO). In the second stage, the power flow is calculated and the constraints are verified, then adjusting measures will be taken. The proposed method is conducted on the IEEE 69-bus test system with 6 DGs and the results are compared with other methods. Moreover, for real time verification, the obtained results are simulated by DIgSILENT/Power Factory software package. The simulation results suggest that the proposed algorithm is a valid method for the intentional islanding process.

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.02.003
      Issue No: Vol. 35 (2017)
  • 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
      Pages: 26 - 40
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      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: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.02.004
      Issue No: Vol. 35 (2017)
  • Bird mating optimizer for structural damage detection using a hybrid
           objective function
    • Authors: J.J. Zhu; M. Huang; Z.R. Lu
      Pages: 41 - 52
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      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: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.02.006
      Issue No: Vol. 35 (2017)
  • Service allocation in the cloud environments using multi-objective
           particle swarm optimization algorithm based on crowding distance
    • Authors: Fereshteh Sheikholeslami; Nima Jafari Navimipour
      Pages: 53 - 64
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      Author(s): Fereshteh Sheikholeslami, Nima Jafari Navimipour
      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: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.02.007
      Issue No: Vol. 35 (2017)
  • A niche GSA method with nearest neighbor scheme for multimodal
    • Authors: Pourya Haghbayan; Hossein Nezamabadi-pour; Shima Kamyab
      Pages: 78 - 92
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      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: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.03.002
      Issue No: Vol. 35 (2017)
  • 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
      Pages: 93 - 110
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      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: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.02.005
      Issue No: Vol. 35 (2017)
  • Comments on “Evolutionary and GPU computing for topology
           optimization of structures”
    • Authors: David Guirguis
      Pages: 111 - 113
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      Author(s): David Guirguis

      PubDate: 2017-09-03T21:03:58Z
      DOI: 10.1016/j.swevo.2017.01.001
      Issue No: Vol. 35 (2017)
  • Detecting composite communities in multiplex networks: a multilevel
           memetic algorithm
    • Authors: Lijia Ma; Maoguo Gong; Jianan Yan; Wenfeng Liu; Shanfeng Wang
      Abstract: Publication date: Available online 28 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Lijia Ma, Maoguo Gong, Jianan Yan, Wenfeng Liu, Shanfeng Wang
      Nowadays, many systems can be well represented by multiplex networks, in which entities can communicate with each other on multiple layers. A multiplex network under each layer has its own communities (i.e., a higher-order organization with a group of similar nodes) while it has a composite structure which is most likely to describe its community structures at all layers. Many algorithms have been proposed to detect communities in unweighted single-layered networks, but most of them cannot be well applied to detect composite communities in multiplex networks. The aim of this paper is to detect composite communities in weighted multiplex networks using a multilevel memetic algorithm. First, a simplified multiplex modularity is adopted for evaluating the fitness of composite communities, and then the community detection problem in multiplex networks is modeled as a combinational optimization problem. Second, we devise a multilevel memetic algorithm that combines a network-specific genetic algorithm with problem-specific multilevel local search operators. In the presented algorithm, the network-specific knowledge (i.e., the layer neighborhood and the consensus neighborhood) and the problem-specific information (i.e., the fast computation of multiplex modularity under each local refinement) are adopted to guide its search processes. Last, extensive experiments are performed on eight real-world networks ranging from social, transport, financial to genetic areas, and the results demonstrate that our algorithm discoveries composite communities in multiplex networks more accurately than the state-of-the-art.

      PubDate: 2017-10-06T01:29:53Z
      DOI: 10.1016/j.swevo.2017.09.012
  • Opposition Based Learning: A literature review
    • Authors: Sedigheh Mahdavi; Shahryar Rahnamayan; Kalyanmoy Deb
      Abstract: Publication date: Available online 23 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Sedigheh Mahdavi, Shahryar Rahnamayan, Kalyanmoy Deb
      Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the opposite relationship among entities. In 2005, for the first time the concept of opposition was introduced which has attracted a lot of research efforts in the last decade. Variety of soft computing algorithms such as, optimization methods, reinforcement learning, artificial neural networks, and fuzzy systems have already utilized the concept of OBL to improve their performance. This survey has been conducted on three classes of OBL attempts: a) theoretical, including the mathematical theorems and fundamental definitions, b) developmental, focusing on the design of the special OBL-based schemes, and c) real-world applications of OBL. More than 380 papers in a variety of disciplines are surveyed and also a comprehensive set of promising directions are discussed in detail.

      PubDate: 2017-09-28T00:21:53Z
      DOI: 10.1016/j.swevo.2017.09.010
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: October 2017
      Source:Swarm and Evolutionary Computation, Volume 36

      PubDate: 2017-09-21T16:30:05Z
  • Parameter optimization and speed control of switched reluctance motor
           based on evolutionary computation methods
    • Authors: Jia-Jun Wang
      Abstract: Publication date: Available online 19 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jia-Jun Wang
      Because of the double–salient structure and switching mode of switched reluctance motor (SRM), it is very difficult to acquire the analytical model for the SRM. The current-sharing method (CSM) is an effective inner-current loop designing strategy, which makes the high performance control of the SRM become possible without application of its mathematical model. However, there are six control parameters that need to be tuned in the CSM. If the PID controller is adopted in the speed loop, there will exist nine parameters that need to be tuned in the speed control of the SRM. It is a challenge work to tune nine parameters with manual trial-and-error method. To alleviate the difficulties of the parameter tuning for the SRM control, three types of evolutionary computation methods are applied in the parameter optimization of the SRM, which include differential evolution (DE) algorithm, Big Bang–Big Crunch (BBBC) algorithm and particle swarm optimization (PSO). The comparison of the optimization performance among the proposed evolutionary computation methods are demonstrated with Matlab simulation. Simulation results certify the feasibility and effectiveness of the proposed methods in the parameter optimization and speed control of the SRM.

      PubDate: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.09.004
  • On botnet detection with genetic programming under streaming data label
           budgets and class imbalance
    • Authors: Sara Khanchi; Ali Vahdat; Malcolm I. Heywood; A. Nur Zincir-Heywood
      Abstract: Publication date: Available online 19 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Sara Khanchi, Ali Vahdat, Malcolm I. Heywood, A. Nur Zincir-Heywood
      Algorithms for constructing models of classification under streaming data scenarios are becoming increasingly important. In order for such algorithms to be applicable under ‘real-world’ contexts we adopt the following objectives: 1) operate under label budgets, 2) make label requests without recourse to true label information, and 3) robustness to class imbalance. Specifically, we assume that model building is only performed using the content of a Data Subset (as in active learning). Thus, the principle design decisions are with regard to the definitions employed for sampling and archiving policies. Moreover, these policies should operate without prior information regarding the distribution of classes, as this varies over the course of the stream. A team formulation for genetic programming (GP) is assumed as the generic model for classification in order to support incremental changes to classifier content. Benchmarking is conducted with thirteen real-world Botnet datasets with label budgets of the order of 0.5–5% and significant amounts of class imbalance. Specific recommendations are made for detecting the costly minor classes under these conditions. Comparison with current approaches to streaming data under label budgets supports the significance of these findings.

      PubDate: 2017-09-21T16:30:05Z
      DOI: 10.1016/j.swevo.2017.09.008
  • An Insight to the Performance of Estimation of Distribution Algorithm for
           Multiple Line Outage Identification
    • Authors: A. Ahmed; Q. Khan; M. Naeem; M. Iqbal; A. Anpalagan; M. Awais
      Abstract: Publication date: Available online 14 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): A. Ahmed, Q. Khan, M. Naeem, M. Iqbal, A. Anpalagan, M. Awais
      Realtime information relating to line outages has significant importance to pre-empt against the the power system blackouts. Realtime information can be obtained by using phasor measurement units (PMUs) facilitating the realtime synchronized observations of voltage and current phasors at buses being monitored. Different optimization formulations including but not limited to linear, integer, stochastic, mixed integer and NP hard combinatorial optimization have been used to manipulate these phasor measurements for the detection of line outages. Single and double line outages can be addressed using combinatorial optimization but these are infeasible to apply for the detection of multiple line outages as the increased number of lines increases computational complexity. To alleviate the exponentially increased complexities of these combinatorial optimization problems, while investigating for multiple line outage, evolutionary, Estimation of Distribution Algorithm is used. This method gives near optimal solution in which computational complexity and time is reduced efficiently. In this paper we scrutinize the use of phasor angle measurements to detect multiple power line outages. The proposed EDA is compared with binary particle swarm optimization (BPSO) algorithm, adaptive BPSO and genetic algorithm (GA) in terms of line outage detection performance, fitness convergence w.r.t. iterations and time consumption. The simulation results depict that the proposed EDA outperforms the other state of the art algorithms.

      PubDate: 2017-09-16T13:42:43Z
      DOI: 10.1016/j.swevo.2017.09.006
  • Optimal Planning of Distributed Energy Resources in Harmonics Polluted
           Distribution System
    • Authors: Manoj Kumawat; Nitin Gupta; Naveen Jain; R.C. Bansal
      Abstract: Publication date: Available online 13 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Manoj Kumawat, Nitin Gupta, Naveen Jain, R.C. Bansal
      In this study, harmonics related to power quality issue and energy demand growth are considered simultaneously towards the realistic planning of medium voltage radial distribution system. Mostly, harmonics of distribution networks are produced in the presence of non-linear loads. Therefore, Distributed Energy Resource (DERs) can be placed to mitigate the harmonic distortions and to supply the required system energy demand. This paper presents a Modified Group experience of Teaching Learning Based Optimization approach, which can deal with allocation of DERs efficaciously in distorted and non-distorted radial distribution networks. The effectiveness of the proposed approach is validated on standards 33-bus and 69-bus test systems along with 83-bus (Taiwan Power Company) practical radial non-distorted distribution system. The results are compared with already well-established existing methods as suggested in the literature. Further, the proposed algorithm is applied to DER planning considering harmonics generating loads in above-mentioned test systems. The results with linear as well as non-linear loads on all three test systems prove that the proposed strategy can be a robust approach to enhance the system performance towards mitigating increased load demand within the constraints of the distribution system.

      PubDate: 2017-09-16T13:42:43Z
      DOI: 10.1016/j.swevo.2017.09.005
  • 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
  • Artificial Life Based on Boids Model and Evolutionary Chaotic Neural
           Networks for Creating Artworks
    • Authors: Tae Jong Choi; Chang Wook Ahn
      Abstract: Publication date: Available online 9 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Tae Jong Choi, Chang Wook Ahn
      In this paper, we propose a multi-agent based art production framework. In existing artwork creation systems, images were generated using artificial life and evolutionary computation approaches. In artificial life, swarm intelligence or Boids model, and in evolutionary computation, genetic algorithm or genetic programming are commonly used to create images. These automated artwork creation systems make it easy to create artistic images even if the users are not professional artists. Despite the high possibility of these creation systems, however, much research has not been done so far. In this paper, we propose an art production framework that generates images using multi-agents with chaotic dynamics features. Agents act on the canvas following the three rules of Boids model. In addition, each agent possesses a chaotic neural network which trained by differential evolution algorithm, so that colors can be evolved to represent a better style. As a result, we propose an art production framework for generating processing artworks that contain highly complex dynamics. Finally, we created the glitch artworks using the proposed framework, which shows a new glitch style.

      PubDate: 2017-09-10T09:28:23Z
      DOI: 10.1016/j.swevo.2017.09.003
  • 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
  • Fuzzy Self-Tuning PSO: A Settings-Free Algorithm for Global Optimization
    • Authors: Marco S. Nobile; Paolo Cazzaniga; Daniela Besozzi; Riccardo Colombo; Giancarlo Mauri; Gabriella Pasi
      Abstract: Publication date: Available online 7 September 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Marco S. Nobile, Paolo Cazzaniga, Daniela Besozzi, Riccardo Colombo, Giancarlo Mauri, Gabriella Pasi
      Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective methods for non-linear and complex high-dimensional problems. Since PSO performance strongly depends on the choice of its settings (i.e., inertia, cognitive and social factors, minimum and maximum velocity), Fuzzy Logic (FL) was previously exploited to select these values. So far, FL-based implementations of PSO aimed at the calculation of a unique settings for the whole swarm. In this work we propose a novel self-tuning algorithm—called Fuzzy Self-Tuning PSO (FST-PSO)—which exploits FL to calculate the inertia, cognitive and social factor, minimum and maximum velocity independently for each particle, thus realizing a complete settings-free version of PSO. The novelty and strength of FST-PSO lie in the fact that it does not require any expertise in PSO functioning, since the behavior of every particle is automatically and dynamically adjusted during the optimization. We compare the performance of FST-PSO with standard PSO, Proactive Particles in Swarm Optimization, Artificial Bee Colony, Covariance Matrix Adaptation Evolution Strategy, Differential Evolution and Genetic Algorithms. We empirically show that FST-PSO can basically outperform all tested algorithms with respect to the convergence speed and is competitive concerning the best solutions found, noticeably with a reduced computational effort.

      PubDate: 2017-09-10T09:28:23Z
      DOI: 10.1016/j.swevo.2017.09.001
  • 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
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35

      PubDate: 2017-09-03T21:03:58Z
  • Large-scale cooperative co-evolution using niching-based multi-modal
           optimization and adaptive fast clustering
    • Authors: Xingguang Peng; Yapei
      Abstract: Publication date: August 2017
      Source:Swarm and Evolutionary Computation, Volume 35
      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: 2017-09-03T21:03:58Z
  • 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
  • On maximizing reliability of grid transaction processing system
           considering balanced task allocation using social spider optimization
    • Authors: Dharmendra Prasad Mahato; Ravi Shankar Singh
      Abstract: Publication date: Available online 27 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-08-04T18:36:10Z
      DOI: 10.1016/j.swevo.2017.07.011
  • 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
      Abstract: Publication date: Available online 26 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-08-04T18:36:10Z
      DOI: 10.1016/j.swevo.2017.07.012
  • A multi-objective evolutionary artificial bee colony algorithm for
           optimizing network topology design
    • Authors: Amani Saad; Salman A. Khan; Amjad Mahmood
      Abstract: Publication date: Available online 20 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-25T17:58:58Z
      DOI: 10.1016/j.swevo.2017.07.010
  • A Robust Stochastic Fractal Search approach for optimization of the
           surface grinding process
    • Authors: Soheyl Khalilpourazari; Saman Khalilpourazary
      Abstract: Publication date: Available online 15 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.008
  • DABE: Differential Evolution in Analogy-Based Software Development Effort
    • Authors: Tirimula Rao Benala; Rajib Mall
      Abstract: Publication date: Available online 13 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.009
  • 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
      Abstract: Publication date: Available online 12 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.006
  • Evolutionary algorithms based synthesis of low sidelobe hexagonal arrays
    • Authors: Sudipta Das; Rajesh Bera; Durbadal Mandal; Sakti Prasad Ghoshal; Rajib Kar
      Abstract: Publication date: Available online 11 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.003
  • Maximum likelihood estimation for the parameters of skew normal
           distribution using genetic algorithm
    • Authors: Abdullah Yalçınkaya; Birdal Şenoğlu; Ufuk Yolcu
      Abstract: Publication date: Available online 11 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.007
  • Multimodal Continuous Ant Colony Optimization for Multisensor Remote
           Sensing Image Registration with Local Search
    • Authors: Yue Wu; Wenping Ma; Qiguang Miao; Shanfeng Wang
      Abstract: Publication date: Available online 8 July 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Yue Wu, Wenping Ma, Qiguang Miao, Shanfeng Wang
      Due to the large differences between different imaging sensors, multisensor remote sensing image registration is a challenging work. Multisensor remote sensing image registration can be formulated as a multimodal problem, and general optimization methods may get trapped into a local optimum when solving complex multimodal problems. In this paper, we introduce a multimodal continuous ant colony optimization algorithm for multisensor remote sensing image registration, and an efficient optimization method is designed as local search operation. Multimodal continuous ant colony optimization algorithm can preserve high diversity and has the global search ability for multimodal problems. Meanwhile, efficient local search operation can improve the efficiency and provide the accurate result. The experimental results have demonstrated the effectiveness and robustness of the proposed method.

      PubDate: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.004
  • Multi-criteria algorithms for portfolio optimization under practical
    • Authors: Suraj S. Meghwani; Manoj Thakur
      Abstract: Publication date: Available online 5 July 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Suraj S. Meghwani, Manoj Thakur
      Selection of promising assets and allocating capital among them is a crucial part of the financial decision-making process. Modern portfolio theory formulated it as a quadratic optimization problem of maximizing expected returns and minimizing the risk of the portfolio. This problem was modified to incorporate investor's preferences resulting in discrete non-linear search space which cannot be handled by traditional quadratic programming approaches. Relevant literature shows the success of evolutionary algorithms in modelling some of these preferences Multi-criteria algorithms for portfolio optimization under practical constraintsin a constrained optimization problem. This study proposes a candidate generation procedure and a repair mechanism for practical portfolio optimization model in multi-objective evolutionary algorithm (MOEA) settings. Both these methods together can handle a larger class of constraints namely cardinality, pre-assignment, budget, quantity (floor and ceiling) and round-lot constraints. Proposed methods can easily be incorporated into existing evolutionary algorithms. To evaluate their effectiveness, four MOEAs namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Global Weighting Achievement Scalarizing Function Genetic Algorithm (GWASFGA) and Pareto Envelope-based Selection Algorithm-II (PESA-II) have been adapted and their capability of approximating unconstrained efficient frontier are discussed. For empirical testing, seven datasets involving maximum up to 1290 assets are used. All the adapted algorithms are compared and evaluated on the basis of five well-known performance metrics for MOEAs. The potential of our adapted algorithms is presented in comparison with existing MOEAs for the identical problems.

      PubDate: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.06.005
  • Accelerated multi-gravitational search algorithm for size optimization of
           truss structures
    • Authors: Mohsen Khatibinia; Hessam Yazdani
      Abstract: Publication date: Available online 4 July 2017
      Source:Swarm and Evolutionary Computation
      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: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.001
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