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Journal Cover Swarm and Evolutionary Computation
  [SJR: 2.167]   [H-I: 22]   [3 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2210-6502
   Published by Elsevier Homepage  [3043 journals]
  • 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
           Estimation
    • 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
           conditions
    • 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
           constraints
    • 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
       
  • Adaptive artificial immune networks for mitigating DoS flooding attacks
    • Authors: Jorge Maestre Vidal; Ana Lucila Sandoval Orozco; Luis Javier García Villalba
      Abstract: Publication date: Available online 4 July 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba
      Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD’99, CAIDA’07 and CAIDA’08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.

      PubDate: 2017-07-19T17:40:58Z
      DOI: 10.1016/j.swevo.2017.07.002
       
  • Weibull-Based Scaled-Differences Schema for Differential Evolution
    • Authors: Miguel
      Abstract: Publication date: Available online 1 July 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Miguel Cárdenas-Montes
      Differential Evolution is one of the most efficient real-parameter optimization algorithm. It is based on the application of the scaled difference of a pair of population members to another population member, all of them distinct. Diverse variants have been proposed within this schema. In this work, the statistical distribution of these differences of high-performance variants of differential evolution is modelled through a Weibull probability distribution. From the application of this model to diverse differential evolution variants and benchmark functions, a pattern for the most efficient variants can be drawn. As a consequence, a variant where the scaled differences are replaced by random numbers generated from a Weibull distribution is proposed and evaluated.

      PubDate: 2017-07-03T08:20:13Z
       
  • An improved migrating birds optimization for an integrated lot-streaming
           flow shop scheduling problem
    • Authors: Tao Meng; Quan-Ke Pan; Jun-Qing Li; Hong-Yan Sang
      Abstract: Publication date: Available online 28 June 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Tao Meng, Quan-Ke Pan, Jun-Qing Li, Hong-Yan Sang
      Lot-streaming is an effective technology to enhance the production efficiency by splitting a job or a lot into several sublots. It is commonly assumed that lot-splitting (i.e. job-splitting) is specified in advance and fixed during the optimization procedure in recent studies on lot-streaming flow shop scheduling problems. In many real-world production processes, however, it is not easy to determine the optimal lot-splitting beforehand. Therefore, in this paper we consider an integrated lot-streaming flow shop scheduling problem in which lot-splitting and job scheduling are needed to be optimized simultaneously. We provide a mathematical model for the problem and present an improved migrating birds optimization (IMMBO) to minimize the maximum completion time or makespan. In the IMMBO algorithm, a harmony search based scheme is designed to construct neighborhood of solutions, which makes good use of optimization information from the population and can tune the search scope adaptively. Moreover, a leaping mechanism is introduced to avoid being trapped in the local optimum. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify the effectiveness of the proposed IMMBO algorithm.

      PubDate: 2017-07-03T08:20:13Z
      DOI: 10.1016/j.swevo.2017.06.003
       
  • A Survey on Multi-objective Evolutionary Algorithms for the Solution of
           the Environmental/Economic Dispatch Problems
    • Authors: B.Y. Qu; Y.S. Zhu; Y.C. Jiao; M.Y. Wu; P.N. Suganthan; J.J. Liang
      Abstract: Publication date: Available online 23 June 2017
      Source:Swarm and Evolutionary Computation
      Author(s): B.Y. Qu, Y.S. Zhu, Y.C. Jiao, M.Y. Wu, P.N. Suganthan, J.J. Liang
      Development of efficient multi-objective evolutionary algorithms (MOEAs) has provided effective tools to solve environmental/economic dispatch (EED) problems. EED is a highly constrained complex bi-objective optimization problem. Since 1990s, numerous publications have reported the applications of MOEAs to solve the EED problems. This paper surveys the state-of-the-art of research related to this direction. It covers topics of typical MOEAs, classical EED problems, Dynamic EED problems, EED problems incorporating wind power, EED problems incorporating electric vehicles and EED problems within micro-grids. In addition, some potential directions for future research are also presented.

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

      PubDate: 2017-06-17T07:51:46Z
      DOI: 10.1016/j.swevo.2017.05.011
       
  • A knowledge-guided multi-objective fruit fly optimization algorithm for
           the multi-skill resource constrained project scheduling problem
    • Authors: Ling Wang; Xiao-long Zheng
      Abstract: Publication date: Available online 8 June 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Ling Wang, Xiao-long Zheng
      In this paper, a knowledge-guided multi-objective fruit fly optimization algorithm (MOFOA) is proposed for the multi-skill resource-constrained project scheduling problem (MSRCPSP) with the criteria of minimizing the makespan and the total cost simultaneously. First, a solution is represented by two lists, i.e. resource list and task list. Second, the minimum total cost rule is designed for the initialization according to the property of the problem. Third, the smell-based search is implemented via the neighborhood based search operators that are specially designed for the MSRCPSP, while the vision-based search adopts the technique for the order preference by similarity to an ideal solution (TOPSIS) and the non-dominated sorting collaboratively to complete the multi-objective evaluation. In addition, a knowledge-guided search procedure is introduced to enhance the exploration of the FOA. Finally, the design-of-experiment (DOE) method is used to investigate the effect of parameter setting, and numerical tests based on benchmark instances are carried out. The results compared to other algorithms demonstrate the effectiveness of the MOFOA with knowledge-guided search in solving the multi-objective MSRCPSP.

      PubDate: 2017-06-13T07:37:16Z
      DOI: 10.1016/j.swevo.2017.06.001
       
  • Inside Front Cover: Editorial board
    • Abstract: Publication date: June 2017
      Source:Swarm and Evolutionary Computation, Volume 34


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

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

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.010
       
  • Evolutionary heterogeneous clustering for rating prediction based on user
           collaborative filtering
    • Authors: Jianrui Chen; Uliji; Hua Wang; Zaizai Yan
      Abstract: Publication date: Available online 25 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jianrui Chen, Uliji, Hua Wang, Zaizai Yan
      Recommender systems play an important role in our life, which would help users to find what they are interested in. Collaborative filtering is the most widely used and successful method for personalized recommendation. In this paper, a novel heterogeneous evolutionary clustering is presented. The goal of our algorithm is to gather users with similar interest into the same cluster and to help users find items that fit their personal tastes best. The suggestions from friends with similar interest may be adopted with high probability. Firstly, items and users are regarded as heterogeneous individuals in the network. According to the constructed network model, states of individuals evolve over time. Individuals with higher scores would cluster into together and individuals with lower scores would get away. After many iterations, states of items and users would be stable. In light of stable states of heterogeneous individuals, they are clustered into several groups. Secondly, user-based collaborative filtering are adopted in each cluster. Similarities between individuals only in same cluster are computed not for all individuals in system. The target rating is calculated according to user-based collaborative filtering in its cluster. Diverse simulations show the efficiency of our proposed methods. Moreover, the presented method gains better prediction results than two existing preferable algorithms.

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

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

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.006
       
  • An effective invasive weed optimization algorithm for scheduling
           semiconductor final testing problem
    • Authors: Hong-Yan Sang; Pei-Yong Duan; Jun-Qing Li
      Abstract: Publication date: Available online 19 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Hong-Yan Sang, Pei-Yong Duan, Jun-Qing Li
      In this paper, we address a semiconductor final testing problem from the semiconductor manufacturing process. We aim to determine both the assignment of machines and the sequence of operations on all the machines so as to minimize makespan. We present a cooperative co-evolutionary invasive weed optimization (CCIWO) algorithm which iterates with two coupled colonies, one of which addresses the machine assignment problem and the other deals with the operation sequence problem. To well balance the search capability of the two colonies, we adopt independent size setting for each colony. We design the reproduction and spatial dispersal methods for both the colonies by taking advantage of the information collected during the search process and problem-specific knowledge. Extensive experiments and comparison show that the proposed CCIWO algorithm performs much better than the state-of-the-art algorithms in the literature for solving the semiconductor final testing scheduling problem with makespan criteria.

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.05.007
       
  • Reply to comments on “Evolutionary and GPU Computing for Topology
           Optimization of Structures”
    • Authors: Deepak Sharma
      Abstract: Publication date: Available online 17 May 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Deepak Sharma
      This short communication is in reply to the comments made on the published article on “Evolutionary and GPU Computing for Topology Optimization of Structures”.

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

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

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

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

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

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

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

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

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

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

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

      PubDate: 2017-06-03T06:57:45Z
      DOI: 10.1016/j.swevo.2017.03.004
       
  • Multi-objective two-level swarm intelligence approach for multiple RNA
           sequence-structure alignment
    • Authors: Soniya Lalwani; Rajesh Kumar; Kusum Deep
      Abstract: Publication date: Available online 22 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Soniya Lalwani, Rajesh Kumar, Kusum Deep
      This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.

      PubDate: 2017-02-23T00:23:13Z
      DOI: 10.1016/j.swevo.2017.02.002
       
  • Particle Swarm Clustering Fitness Evaluation with Computational Centroids
    • Authors: Jenni Raitoharju; Kaveh Samiee; Serkan Kiranyaz; Moncef Gabbouj
      Abstract: Publication date: Available online 9 February 2017
      Source:Swarm and Evolutionary Computation
      Author(s): Jenni Raitoharju, Kaveh Samiee, Serkan Kiranyaz, Moncef Gabbouj
      In this paper, we propose a new way to carry out fitness evaluation in dynamic Particle Swarm Clustering (PSC) with centroid-based encoding. Generally, the PSC fitness function is selected among the clustering validity indices and most of them directly depend on the cluster centroids. In the traditional fitness evaluation approach, the cluster centroids are replaced by the centroids proposed by a particle position. We propose to first compute the centroids of the corresponding clusters and then use these computational centroids in fitness evaluation. The proposed way is called Fitness Evaluation with Computational Centroids (FECC). We conducted an extensive set of comparative evaluations and the results show that FECC leads to a clear improvement in clustering results compared to the traditional fitness evaluation approach with most of the fitness functions considered in this study. The proposed approach was found especially beneficial when underclustering is a problem. Furthermore, we evaluated 31 fitness functions based on 17 clustering validity indices using two PSC methods over a large number of synthetic and real data sets with varying properties. We used three different performance criteria to evaluate the clustering quality and found out that the top three fitness functions are Xu index, WB index, and Dunn variant DU 23 applied using FECC. These fitness functions were consistently performing well for both PSC methods, for all data distributions, and according to all performance criteria. In all test cases, they were clearly among the better half of the fitness functions and, in the majority of the cases, they were among the top 4 functions. Further guidance for improved fitness function selection in different situations is provided in the paper.

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

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

      PubDate: 2017-01-29T08:55:12Z
      DOI: 10.1016/j.swevo.2017.01.002
       
  • Time series analysis and short-term forecasting of solar irradiation, a
           new hybrid approach
    • Authors: N. Bigdeli; M. Salehi Borujeni; K. Afshar
      Abstract: Publication date: Available online 29 December 2016
      Source:Swarm and Evolutionary Computation
      Author(s): N. Bigdeli, M. Salehi Borujeni, K. Afshar
      In this paper, nonlinear time series analysis and short-term prediction of solar irradiation were considered, simultaneously. The proposed methodology is to employ time series analysis methods as well as swarm and evolutionary algorithms in conjunction with well-known regression, fuzzy and neural network model structures to develop a simple but efficient and applicable model for solar irradiation forecasting. The employed experimental data was the hourly solar irradiation of Qazvin city in Iran for five years. At first, the solar irradiation data was normalized using the daily clear sky irradiation data which is an annually periodic time series. Then, the properties of normalized solar irradiation were characterized via time series analysis methods such as recurrence plots, autocorrelation and mutual information analysis. Based on these analyses, each year was divided into two seasons, the sunny and cloudy seasons which are noticeably different in dynamics. Next, a hybrid but simple model was developed to predict the solar irradiation in different seasons. For the sunny season, an optimized multivariate regression model was proposed; and for the cloudy season a bi-level model consisting of an optimized regression model and ANFIS was developed. The model parameters were tuned optimally by various evolutionary algorithms being GA, PSO, ABC, COA, and flower pollination algorithm (FPA). A Fourier-type model was also developed for modeling of the clear sky data. The results showed the out-performance of FPA method in tuning of the model parameters and convergence time. Besides, the performance of the proposed bi-level model was evaluated in comparison with some other model structures such as artificial neural networks, ANFIS networks, LSE-regression models, LS-support vector machines model, etc. The results showed that the proposed method performs considerably better than the other methods in forecasting the solar irradiation time series in both sunny and cloudy seasons.

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