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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  [3049 journals]
  • Global-best brain storm optimization algorithm
    • Authors: Mohammed El-Abd
      Pages: 27 - 44
      Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37
      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: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.05.001
      Issue No: Vol. 37 (2017)
       
  • Multiobjective evolutionary algorithm based on vector angle neighborhood
    • Authors: Roman Denysiuk; António Gaspar-Cunha
      Pages: 45 - 57
      Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37
      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: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.05.005
      Issue No: Vol. 37 (2017)
       
  • 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
      Pages: 58 - 72
      Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37
      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: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.05.002
      Issue No: Vol. 37 (2017)
       
  • Modified cuckoo search algorithm for multiobjective short-term
           hydrothermal scheduling
    • Authors: Thang Trung Nguyen; Dieu Ngoc Vo
      Pages: 73 - 89
      Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37
      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: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.05.006
      Issue No: Vol. 37 (2017)
       
  • Sizing and topology optimization of truss structures using genetic
           programming
    • Authors: Hirad Assimi; Ali Jamali; Nader Nariman-zadeh
      Pages: 90 - 103
      Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37
      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: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.05.009
      Issue No: Vol. 37 (2017)
       
  • Multi-criteria algorithms for portfolio optimization under practical
           constraints
    • Authors: Suraj S. Meghwani; Manoj Thakur
      Pages: 104 - 125
      Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37
      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: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.06.005
      Issue No: Vol. 37 (2017)
       
  • 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)
       
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: December 2017
      Source:Swarm and Evolutionary Computation, Volume 37


      PubDate: 2017-11-18T15:54:35Z
       
  • A semi-supervised Genetic Programming method for dealing with noisy labels
           and hidden overfitting
    • Authors: Sara Silva; Leonardo Vanneschi; Ana I.R. Cabral; Maria J. Vasconcelos
      Abstract: Publication date: Available online 15 November 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Sara Silva, Leonardo Vanneschi, Ana I.R. Cabral, Maria J. Vasconcelos
      Data gathered in the real world normally contains noise, either stemming from inaccurate experimental measurements or introduced by human errors. Our work deals with classification data where the attribute values were accurately measured, but the categories may have been mislabeled by the human in several sample points, resulting in unreliable training data. Genetic Programming (GP) compares favorably with the Classification and Regression Trees (CART) method, but it is still highly affected by these errors. Despite consistently achieving high accuracy in both training and test sets, many classification errors are found in a later validation phase, revealing a previously hidden overfitting to the erroneous data. Furthermore, the evolved models frequently output raw values that are far from the expected range. To improve the behavior of the evolved models, we extend the original training set with additional sample points where the class label is unknown, and devise a simple way for GP to use this additional information and learn in a semi-supervised manner. The results are surprisingly good. In the presence of the exact same mislabeling errors, the additional unlabeled data allowed GP to evolve models that achieved high accuracy also in the validation phase. This is a brand new approach to semi-supervised learning that opens an array of possibilities for making the most of the abundance of unlabeled data available today, in a simple and inexpensive way.

      PubDate: 2017-11-18T15:54:35Z
      DOI: 10.1016/j.swevo.2017.11.003
       
  • A tissue P system based evolutionary algorithm for multi-objective VRPTW
    • Authors: Wenbo Dong; Kang Zhou; Huaqing Qi; Cheng He; Jun Zhang
      Abstract: Publication date: Available online 8 November 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Wenbo Dong, Kang Zhou, Huaqing Qi, Cheng He, Jun Zhang
      Multi-objective vehicle routing problem with time windows (VRPTW) has important applications in engineering and computer science, and it is a NP-hard problem. In the last decade, numerous new methods for multi-objective VRPTW have sprung up. However, the calculation speed of most algorithms is not fast enough, and on the other hand, these algorithms did not give a complete Pareto optimal front, although their results are excellent. Hence, in this paper, a tissue P system with three cells based MOEA, termed PDVA, is proposed to solve the multi-objective VRPTW. In PDVA, two mechanisms, the discrete glowworm evolution mechanism (DGEM) and the variable neighborhood evolution mechanism (VNEM), are used as sub-algorithms in two cells respectively to balance the exploration and exploitation reasonably. Simultaneously, some special strategies are used to enhance the performance of the proposed algorithm. The following experiments are presented to test the proposed algorithm. First, the influence of the parameters on the performance of the algorithm is investigated. Second, the validity of the algorithm is highlighted when compared to the DGEM-VNEM algorithm. Third, the quality and diversity of the solutions are improved when compared to the other popular algorithms. These results and comparisons on test instances demonstrate the competitiveness of PDVA in solving multi-objective VRPTW in terms of both quantity and speed.

      PubDate: 2017-11-10T14:39:12Z
      DOI: 10.1016/j.swevo.2017.11.001
       
  • Population topologies for particle swarm optimization and differential
           evolution
    • Authors: Nandar Lynn; Mostafa Z. Ali; Ponnuthurai Nagaratnam Suganthan
      Abstract: Publication date: Available online 6 November 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Nandar Lynn, Mostafa Z. Ali, Ponnuthurai Nagaratnam Suganthan
      Over the last few decades, many population-based swarm and evolutionary algorithms were introduced in the literature. It is well known that population topology or sociometry plays an important role in improving the performance of population-based optimization algorithms by enhancing population diversity when solving multiobjective and multimodal problems. Many population structures and population topologies were developed for particle swarm optimization and differential evolutionary algorithms. Therefore, a comprehensive review of population topologies developed for PSO and DE is carried out in this paper. We anticipate that this survey will inspire researchers to integrate the population topologies into other nature inspired algorithms and to develop novel population topologies for improving the performances of population-based optimization algorithms for solving single objective optimization, multiobjective optimization and other classes of optimization problems.

      PubDate: 2017-11-10T14:39:12Z
      DOI: 10.1016/j.swevo.2017.11.002
       
  • Handling time-varying constraints and objectives in dynamic evolutionary
           multi-objective optimization
    • Authors: Radhia Azzouz; Slim Bechikh; Lamjed Ben Said; Walid Trabelsi
      Abstract: Publication date: Available online 4 November 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Radhia Azzouz, Slim Bechikh, Lamjed Ben Said, Walid Trabelsi
      Recently, several researchers within the evolutionary and swarm computing community have been interested in solving dynamic multi-objective problems where the objective functions, the problem's parameters, and/or the constraints may change over time. According to the related literature, most works have focused on the dynamicity of objective functions, which is insufficient since also constraints may change over time along with the objectives. For instance, a feasible solution could become infeasible after a change occurrence, and vice versa. Besides, a non-dominated solution may become dominated, and vice versa. Motivated by these observations, we devote this paper to focus on the dynamicity of both: (1) problem's constraints and (2) objective functions. To achieve our goal, we propose a new self-adaptive penalty function and a new feasibility driven strategy that are embedded within the NSGA-II and that are applied whenever a change is detected. The feasibility driven strategy is able to guide the search towards the new feasible directions according to the environment changes. The empirical results have shown that our proposal is able to handle various challenges raised by the problematic of dynamic constrained multi-objective optimization. Moreover, we have compared our new dynamic constrained NSGA-II version, denoted as DC-MOEA, against two existent dynamic constrained evolutionary algorithms. The obtained results have demonstrated the competitiveness and the superiority of our algorithm on both aspects of convergence and diversity.

      PubDate: 2017-11-10T14:39:12Z
      DOI: 10.1016/j.swevo.2017.10.005
       
  • A cluster-based dissimilarity learning approach for localized fault
           classification in Smart Grids
    • Authors: Enrico De Santis; Antonello Rizzi; Alireza Sadeghian
      Abstract: Publication date: Available online 31 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Enrico De Santis, Antonello Rizzi, Alireza Sadeghian
      Modeling and recognizing faults and outages in a real-world power grid is a challenging task, in line with the modern concept of Smart Grids. The availability of Smart Sensors and data networks allows to “x-ray scan” the power grid states. The present paper deals with a recognition system of fault states described by heterogeneous information in the real-world power grid managed by the ACEA company in Italy. The pattern recognition problem is tackled as two-class classification problem using a Clustering-Evolutionary Computing approach and it is able to generate together with a Boolean decision also a score value. The last is computed through a fuzzy membership function and output values are interpreted as a reliability measure for the Boolean decision rule. As many real-world pattern recognition applications, the starting feature space is structured and the custom based dissimilarity measure adopted leads to a non-Euclidean dissimilarity matrix. Hence, a comparison of the classification performances between the proposed two-class classifier system and the well-known Support Vector Machine, on the data set at hands, is performed using a suitable kernel designed for the non-Euclidean case.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.007
       
  • A cost-benefit local search coordination in multimeme differential
           evolution for constrained numerical optimization problems
    • Authors: Saúl Domínguez-Isidro; Efrén Mezura-Montes
      Abstract: Publication date: Available online 31 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Saúl Domínguez-Isidro, Efrén Mezura-Montes
      This paper introduces an adaptive local search coordination for a multimeme Differential Evolution to constrained numerical optimization problems. The proposed approach associates a pool of direct local search operators within the standard Differential Evolution. The coordination mechanism consists of a probabilistic method based on a cost-benefit scheme, and it is aimed to regulate the activation probability of every local search operator during the evolutionary cycle of the global search. Also, the method adopts the ɛ -constrained method as a constraint-handling technique. The proposed approach is tested on thirty-six well-known benchmark problems. Numerical results show that the proposed method is suitable to coordinate a set of local search operators adequately within a memetic scheme for constrained search spaces.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.006
       
  • Three pseudo-utility ratio-inspired particle swarm optimization with local
           search for multidimensional knapsack problem
    • Authors: Mingchang Chih
      Abstract: Publication date: Available online 31 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Mingchang Chih
      In this study, a three-ratio self-adaptive check and repair operator-inspired particle swarm optimization (3R-SACRO-PSO) with neighborhood local search is developed to solve the multidimensional knapsack problem (MKP). The proposed 3R-SACRO-PSO systematically alters substitute pseudo-utility ratios as the PSO method is executed. In addition, a local search scheme is introduced to improve solution quality. The proposed 3R-SACRO-PSO algorithm is tested using 168 different widely used benchmarks from the OR-Library to demonstrate and validate its performance. The control parameters for the performance test are determined through the Taguchi method. Experimental results parallel those of other PSO algorithms, and statistical test results show that the quality and efficiency of the proposed 3R-SACRO are better than those of the two-ratio SACRO method. Moreover, the proposed 3R-SACRO-PSO is on par with state-of-the-art PSO approaches. Thus, introducing the third pseudo-utility ratio into SACRO improves the performance of SACRO-based PSO. The neighborhood local search scheme further improves the solution quality in handling MKPs.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.008
       
  • A multi-objective particle swarm optimization algorithm for community
           detection in complex networks
    • Authors: Shadi Rahimi; Alireza Abdollahpouri; Parham Moradi
      Abstract: Publication date: Available online 31 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Shadi Rahimi, Alireza Abdollahpouri, Parham Moradi
      Community structure is an interesting feature of complex networks. The problem of community detection has attracted many research efforts in recent years. Most of the algorithms developed for this purpose take advantage of single-objective optimization methods which may be ineffective for complex networks. In this article, a novel multi-objective community detection method based on a modified version of particle swarm optimization, named MOPSO-Net is proposed. Kernel k-means (KKM) and ratio cut (RC) are employed as objective criteria to be minimized. Our innovation in PSO algorithm is changing the moving strategy of particles. Experiments on synthetic and real-world networks confirm a significant improvement in terms of normalized mutual information (NMI) and modularity in comparison with recent similar approaches.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.009
       
  • An aerial robot for rice farm quality inspection with type-2 fuzzy neural
           networks tuned by particle swarm optimization-sliding mode control hybrid
           algorithm
    • Authors: Efe Camci; Devesh Raju Kripalani; Linlu Ma; Erdal Kayacan; Mojtaba Ahmadieh Khanesar
      Abstract: Publication date: Available online 24 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Efe Camci, Devesh Raju Kripalani, Linlu Ma, Erdal Kayacan, Mojtaba Ahmadieh Khanesar
      Agricultural robots, or agrobots, have been increasingly adopted in every aspect of farming from surveillance to fruit harvesting in order to improve the overall productivity over the last few decades. Motivated by compelling growth of agricultural robots in modern farms, in this work, an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions such as wind and gust disturbances as well as positioning errors caused by inertial measurement units and global positioning system. To handle these difficulties, as a model-free and learning control algorithm, type-2 fuzzy neural networks (T2-FNNs) are designed for the control of quadcopter. The novel particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of T2-FNNs. In particular, continuous version of PSO is adopted for the identification of the antecedent part of T2-FNNs while SMC-based update rules are utilized for online learning of the consequent part during control. In the virtual environment, the quadcopter is expected to perform an autonomous flight including agile maneuvers such as steep turning and sudden altitude changes over a rice terrace farm in Longsheng, China. The simulation results for T2-FNNs are compared with the outcome of conventional proportional-derivative (PD) controllers for different case studies. The results show that our method decreases trajectory tracking integral squared error by %26 over PD controllers in the ideal case, while this ratio goes up to %95 under uncertain working conditions.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.003
       
  • Dynamic multi-swarm differential learning particle swarm optimizer
    • Authors: Yonggang Chen; Lixiang Li; Haipeng Peng; Jinghua Xiao; Qingtao Wu
      Abstract: Publication date: Available online 21 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Yonggang Chen, Lixiang Li, Haipeng Peng, Jinghua Xiao, Qingtao Wu
      Because different optimization algorithms have different search behaviors and advantages, hybrid strategy is one of the main research directions to improve the performance of PSO. Inspired by this idea, a dynamic multi-swarm differential learning particle swarm optimizer (DMSDL-PSO) is proposed in this paper. We propose a novel method to merge the differential evolution operator into each sub-swarm of the DMSDL-PSO. Combining the exploration capability of the differential mutation and employing Quasi-Newton method as a local searcher to enhance the exploitation capability, DMSDL-PSO has a good exploration and exploitation capability. According to the characteristics of the DMSDL-PSO, three modified differential mutation operators are discussed. Differential mutation is adopted for the personal historically best particle. Because the velocity updating equation of the particles in PSO has some shortcomings, a modified velocity updating equation is adopted in DMSDL-PSO. In DMSDL-PSO, in which the particles are divided into several small and dynamic sub-swarms. The dynamic change of sub-swarms can promote the information exchange of the whole swarm. In order to test the performance of DMSDL-PSO, 41 benchmark functions are adopted. Lots of numerical experiments are conducted to compare DMSDL-PSO with other popular algorithms. The numerical results demonstrate that DMSDL-PSO performs better on some benchmark functions.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.004
       
  • Novel genetic ensembles of classifiers applied to myocardium dysfunction
           recognition based on ECG signals
    • Abstract: Publication date: Available online 20 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Paweł Pławiak
      This article presents an innovative genetic ensembles of classifiers applied to classification of cardiac disorders (17 classes) based on electrocardiography (ECG) signal analysis. From a social point of view, it is extremely important to prevent heart diseases, which are the most common cause of death worldwide. According to statistical data, 50 million people are at risk for cardiac diseases worldwide. This research collected 744 fragments of ECG signals from the MIT-BIH Arrhythmia database for one lead, MLII, from 29 patients. Novel methodology that consisted of the analysis of longer (10-s) fragments of the ECG signal was used (an average of 13 times less classification). To enhance the characteristic features of the ECG signal, the power spectral density was estimated (using Welchs method and a discrete Fourier transform). In research designed two genetic ensembles of classifiers optimized: by classes and by sets, based on: SVM classifier, 10-fold cross-validation method, layered learning, genetic selection of features, genetic optimization of classifiers parameters and novel genetic training. The best genetic ensemble of classifiers optimized by sets, obtained a classification sensitivity of 17 heart disorders (classes) at a level of 91.40% (64 errors per 744 classifications, accuracy = 98.99%, specificity = 99.46%, time for classification of one sample = 0.0186 [s]). Against the background of the current scientific literature, these results represent some of the best results obtained.

      PubDate: 2017-11-03T14:04:14Z
       
  • A memetic algorithm to optimize critical diameter
    • Authors: Haifeng Du; Jingjing Wang; Xiaochen He; Wei Du
      Abstract: Publication date: Available online 20 October 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Haifeng Du, Jingjing Wang, Xiaochen He, Wei Du
      Diameter is an important index measuring the connectivity and the transfer efficiency of networks. In the process of minimizing APL (Average Path Length) by adding edges, a fact was found that APL begins to linearly decline after the number of added edges increases to a turning point, which also leads the network diameter decreases to 2. At this point, the state of network was defined as a critical state. Furthermore, we put forward the new concept of critical diameter and explore its properties. Memetic algorithm which combines the advantages of both genetic algorithm and local search has shown good performance in solving combinational explosion problems. The experimental results showed that an efficient transformation to critical diameter can be achieved by applying the memetic algorithm which proposed in this paper.

      PubDate: 2017-11-03T14:04:14Z
      DOI: 10.1016/j.swevo.2017.10.001
       
  • 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
           Model
    • 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
       
  • An investigation into many-objective optimization on combinatorial
           problems: analyzing the pickup and delivery problem
    • Authors: Abel García-Nájera; Antonio López-Jaimes
      Abstract: Publication date: Available online 4 August 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Abel García-Nájera, Antonio López-Jaimes
      Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several proposals have appeared. An important result states that the problem does not necessarily becomes more difficult while more objectives are considered. Nevertheless, combinatorial problems have not received an appropriate attention, making this an open research area. This investigation takes this subject on by studying a many-objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists of finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, in many applications, some other objectives emerge, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of: (i) the performance of four representative multi-objective evolutionary algorithms on PDP varying the number of objectives, (ii) the properties of the many-objective PDP regarding scalability, i.e. the conflict between each pair of objectives and the proportion of non-dominated solutions as the number of objectives is varied, and finally (iii) the change of PDP's difficulty when the number of objectives is increased. Results show that the regarded objectives are actually in conflict and that the problem is more difficult to solve while more objectives are considered.

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