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Swarm and Evolutionary Computation

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ISSN (Print) 2210-6502
Published by Elsevier

[2556 journals]

An Ant Colony Optimization Technique for Solving Min-Max Multi-Depot Vehicle Routing Problem
- Abstract: Publication date: Available online 18 May 2013
  Source:Swarm and Evolutionary Computation
  Author(s): Koushik Narasimha , Elad Kivelevitch , Balaji Sharma , Manish Kumar
  The Multi-Depot Vehicle Routing Problem (MDVRP) involves minimizing the total distance travelled by vehicles originating from multiple depots so that the vehicles together visit the specified customer locations (or cities) exactly once. This problem belongs to a class of Nondeterministic Polynomial Hard (NP- Hard) problems and has been used in literature as a benchmark for development of optimization schemes. This article deals with a variant of MDVRP, called min-max MDVRP, where the objective is to minimize the tour-length of the vehicle travelling the longest distance in MDVRP. Markedly different from the traditional MDVRP, min-max MDVRP is of specific significance for time-critical applications such as emergency response, where one wants to minimize the time taken to attend any customer. This article presents an extension of an existing ant-colony technique for solving the Single Depot Vehicle Routing Problem (SDVRP) to solve the multiple depots and min-max variants of the problem. First, the article presents the algorithm that solves the min-max version of SDVRP. Then, the article extends the algorithm for min-max MDVRP using an equitable region partitioning approach aimed at assigning customer locations to depots so that MDVRP is reduced to multiple SDVRPs. The proposed method has been implemented in MATLAB for obtaining the solution for the min-max MDVRP with any number of vehicles and customer locations. A comparative study is carried out to evaluate the proposed algorithm's performance with respect to a currently available Linear Programming (LP) based algorithm in literature in terms of the optimality of solution. Based on simulation studies and statistical evaluations, it has been demonstrated that the ant colony optimization technique proposed in this article leads to more optimal results as compared to the existing LP based method.
  
  PubDate: 2013-05-21T19:39:34Z
Using a Self-Adaptive Neighborhood scheme with Crowding Replacement Memory in Genetic Algorithm for Multimodal Optimization
- Abstract: Publication date: Available online 20 May 2013
  Source:Swarm and Evolutionary Computation
  Author(s): S. Kamyab , M. Eftekhari
  In this paper a new GA based niching method using a Self-adaptive Neighborhood scheme with Crowding Replacement Memory (GA_SN_CM) for multimodal optimization is proposed, where, instead of using a niche radius to identify neighborhoods in the population, each individual attempts to select suitable neighbors from the population adaptively. Such neighborhood structure allows eliminating redundant solutions in a neighborhood to increase the diversity of the population which leads the algorithm to explore more solutions. Besides, in order to conserve found niche during the niching procedure, a memory swarm with crowding replacement scheme is used along with the main population. The results of performance comparison between the proposed method and some existing niching techniques over several multimodal benchmark functions demonstrate good performance of GA_SN_CM in improving the niching process.
  
  PubDate: 2013-05-21T19:39:34Z
Optimization of Stacking Ensemble Configurations through Artificial Bee Colony Algorithm
- Abstract: Publication date: Available online 25 April 2013
  Source:Swarm and Evolutionary Computation
  Author(s): P. Shunmugapriya , S. Kanmani
  A Classifier Ensemble combines a finite number of classifiers of same kind or different, trained simultaneously for a common classification task. The Ensemble efficiently improves the generalization ability of the classifier compared to a single classifier. Stacking is one of the most influential ensemble techniques that applies a two level structure of classification namely the base classifiers level and the meta-classifier level. Finding suitable configuration of base level classifiers and the meta-level classifier is always a tedious task and it is domain specific. The Artificial Bee Colony (ABC) Algorithm is a relatively new and popular meta-heuristic search algorithm proved to be successful in solving optimization problems. In this work, we propose the construction of two types of stacking using ABC algorithm: ABC-Stacking1 and ABC-Stacking2. The proposed ABC based stacking is tested using 10 benchmark datasets. The results show that the ABC-Stacking yields promising results and is most useful in selecting the optimal base classifiers configuration and the meta-classifier.
  
  PubDate: 2013-04-27T20:02:45Z
Inside Front Cover: Editorial board
- Abstract: Publication date: June 2013
  Source:Swarm and Evolutionary Computation, Volume 10
  
  PubDate: 2013-04-27T20:02:45Z
A Novel Evolutionary Approach for Load Balanced Clustering Problem for Wireless Sensor Networks
- Abstract: Publication date: Available online 17 April 2013
  Source:Swarm and Evolutionary Computation
  Author(s): Pratyay Kuila , Suneet K. Gupta , Prasanta K. Jana
  Clustering sensor nodes is an effective topology control method to reduce energy consumption of the sensor nodes for maximizing lifetime of Wireless Sensor Networks (WSNs). However, in a cluster based WSN, the leaders (cluster heads) bear some extra load for various activities such as data collection, data aggregation and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a challenging issue for the long run operation of the WSNs. Load balanced clustering is known to be an NP-hard problem for a WSN with unequal load of the sensor nodes. Genetic Algorithm (GA) is one of the most popular evolutionary approach that can be applied for finding the fast and efficient solution of such problem. In this paper, we propose a novel GA based load balanced clustering algorithm for WSN. The proposed algorithm is shown to perform well for both equal as well as unequal load of the sensor nodes. We perform extensive simulation of the proposed method and compare the results with some evolutionary based approaches and other related clustering algorithms. The results demonstrate that the proposed algorithm performs better than all such algorithms in terms of various performance metrics such as load balancing, execution time, energy consumption, number of active sensor nodes, number of active cluster heads and the rate of convergence.
  
  PubDate: 2013-04-19T20:03:01Z
Performance investigations of genetic algorithms on graphics cards
- Abstract: Publication date: Available online 15 April 2013
  Source:Swarm and Evolutionary Computation
  Author(s): Johannes Hofmann , Steffen Limmer , Dietmar Fey
  Genetic algorithms are one of the most adaptable optimization algorithms. Due to their inherent parallelism they seem well suited for the execution on massively parallel hardware such as graphics processing units. In this paper we put this claim to the test by performing comprehensive experiments. We try to find out how well graphics processing units are suited for the task and what parts of genetic algorithms should be executed on them. We focus especially on the new Fermi generation of Nvidia graphics chips. While it is imperative the fitness function be effectively parallelizable on the GPU, because it is the most computational expensive task of the algorithm, results indicate that if this is the case, speedups of several orders of magnitude are possible compared to conventional multi-core CPUs. Our findings also suggest that, starting with the Fermi architecture, all parts of a genetic algorithm should be carried out on the graphics card instead of only part of it.
  
  PubDate: 2013-04-15T20:24:16Z
How can surrogates influence the convergence of evolutionary algorithms?
- Abstract: Publication date: Available online 15 April 2013
  Source:Swarm and Evolutionary Computation
  Author(s): Yu Chen , Xiufen Zou , Weicheng Xie
  Surrogate-assisted evolutionary algorithms have been widely utilized in science and engineering fields, while rare theoretical results were reported on how surrogates influence the performances of evolutionary algorithms (EAs). This paper focuses on theoretical analysis of a (1+1) surrogate-assisted evolutionary algorithm ((1+1)SAEA), which consists of one individual and pre-evaluates a newly-generated candidate using a first-order polynomial model (FOPM) before it is precisely evaluated at each generation. By performing comparisons between a unimodal problem and a multi-model problem, we rigorously estimate the variation of exploitation ability and exploration ability introduced via the FOPM. Theoretical results show that the FOPM employed to pre-evaluate the candidates sometimes accelerate the convergence of evolutionary algorithms, while sometimes prevents the individuals from converging to the global optimal solution. Thus, appropriate adaptive strategies of candidate generation and surrogate control are needed to accelerate the convergence of the (1+1)EA. Then, the accelerating effect of FOPM decreases monotonically with p, the probability of performing precise function evaluation when a candidate is pre-evaluated worse than the present individual.
  
  PubDate: 2013-04-15T20:24:16Z
Memetic Algorithm for Minimum Energy Broadcast Problem in Wireless Ad hoc Networks
- Abstract: Publication date: Available online 10 April 2013
  Source:Swarm and Evolutionary Computation
  
  The problem of finding a broadcast tree with minimum power consumption has received much attention due to two reasons. They are a) the limited bandwidth of wireless networks and b) the nodes are battery operated. This work proposes heuristic memetic algorithm to find minimum energy broadcast tree in wireless ad hoc networks. The simulation results on numerous problem instances confirm that proposed algorithm significantly outperforms several heuristic algorithms in terms of solution quality. The experimental evaluation of memetic algorithm shows that it generally improves over other recently proposed algorithms. By solving well-known benchmark problem instances with 20 and 50 nodes, it also demonstrates the effectiveness of memetic algorithm in terms of computation time.
  
  PubDate: 2013-04-11T19:38:10Z
Formulating particle swarm optimization based membership linear discriminant analysis
- Abstract: Available online 22 March 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  In this paper, we propose the technique for computing the scatter matrices that are used in the linear discriminant analysis using the modified centroids. Modified (weighted) centroids are computed as the weighted mean of all the vectors belonging to the identical cluster. The membership values which are used to compute the weighted centroids are obtained using the fuzzy membership values and the particle swarm optimization. The experiments are performed using the 2-D, 3-D toy clusters and the real database. The experiment results reveal the fact that the detection rate has increased significantly when the classical centroids are replaced with the weighted centroids in LDA.
  
  PubDate: 2013-03-25T21:08:11Z
Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm
- Abstract: Available online 21 March 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  In this paper, optimal thresholds for multi-level thresholding in an image are obtained by maximizing the Tsallis entropy using cuckoo search algorithm. The method is considered as a constrained optimization problem. The solution is obtained through the convergence of a Meta heuristic search algorithm. The proposed algorithm is tested on standard set of images. The results are then compared with that of bacteria foraging optimization (BFO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO) and genetic algorithm (GA). Results are analyzed both qualitatively and quantitatively. It is observed that our results are also encouraging in terms of CPU time and objective function values.
  
  PubDate: 2013-03-21T22:48:35Z
Inside Front Cover: Editorial board
- Abstract: April 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 9
  
  PubDate: 2013-03-17T21:14:11Z
An Unsupervised Multi-Swarm Clustering Technique for Image Segmentation
- Abstract: Available online 4 March 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  Methods based on Particle Swarm Optimization represent efficient tools to solve a wide class of problems. In particular, they have been successfully applied to data clustering and image processing. In this paper a multi-swarm clustering technique to perform an image segmentation is proposed. The search of the grey levels segmenting the image is carried out by a two-stage procedure. The former is performed by a traditional swarm population, moving in the search space according to a minimum distance criterion. The latter exploits a structure composed by identical swarms that refine the solution of the previous step. The combination of the two swarm approaches allows to tackle the drawbacks of the classical paradigm without making use of a complex implementation. The method is unsupervised, since it identifies the actual number of grey levels to segment the image automatically. Such characteristic is fundamental in the application of image segmentation to real cases, where generally the optimal number of centres is not known a priori and the algorithms are required to face possible environment variations. The conducted experiments show that the proposed technique is able to yield adequate segmentations with a limited computational time, proving to be an interesting tool to face cases in which urgent time constraints have to be satisfied.
  
  PubDate: 2013-03-05T21:14:06Z
Leukocyte segmentation in tissue images using differential evolution algorithm
- Abstract: Available online 28 February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  An automatic segmentation of leukocytes can assist pharmaceutical companies to take decisions in the discovery of drugs and encourages for development of automated leukocyte recognition system. Segmentation of leukocytes in tissue images is a complex process due to the presence of various noise effects, large variability in the images and shape of the nuclei. Surprisingly, rare efforts have been made to automate the segmentation of leukocytes in various disease models on Hematoxylin and Eosin (H&E) stained tissue images. The present work proposes a novel strategy based on Differential Evolution (DE) algorithm to segment the leukocytes from the images of mice skin sections stained with H&E staining and acquired at 40x magnification. The proposed strategy is a first inline report used in such type of image database. Further, the proposed strategy is compared with well known segmentation algorithms. The results show that the proposed strategy outperforms the traditional image segmentation techniques.
  
  PubDate: 2013-02-28T21:44:29Z
Optimal design of classic Atkinson engine with dynamic specific heat using adaptive neuro-fuzzy inference system and mutable smart bee algorithm
- Abstract: Available online 17 January 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  In this article, an improved version of Artificial Bee Colony (ABC) algorithm is developed to optimize a multi-modal thermodynamic power system with dynamic specific heat. Since original Karaboga's ABC for constraint problems does not consider the initial population to be feasible, a modified method called “Mutable Smart Bee Algorithm” (MSBA) is used which utilizes mutable heuristic agents. These mutable agents are also capable to maintain their historical memory for the location and quality of food sources. These features have been found as strong elements for mining data in constraint areas. In additions, our implementations reveal that MSBA is faster than Karaboga's method. To elaborate on authenticity of MSBA, several state-of-the-art techniques are used as rival methods to optimize well-known benchmark problems. Then, two main steps are made to optimize Atkinson engine. Firstly, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to identify the dynamic behavior of specific heat. Then, MSBA is hired to design the optimum features of the engine. It is observed that the proposed method is capable to successfully handle the real-life engineering problem as well as the numerical benchmark problems.
  
  PubDate: 2013-01-21T15:08:59Z
Inside Front Cover: Editorial board
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  PubDate: 2013-01-17T03:28:34Z
Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  In this paper, we report the results of our investigation of an evolutionary approach for solving the unequal area multi-objective facility layout problem (FLP) using the variable neighborhood search (VNS) with an adaptive scheme that presents the final layouts as a set of Pareto-optimal solutions. The unequal area FLP comprises a class of extremely difficult and widely applicable optimization problems arising in diverse areas and meeting the requirements for real-world applications. The VNS is an explorative local search method whose basic idea is systematic change of neighborhood within a local search. Traditionally, local search is applied to the solutions of each generation of an evolutionary algorithm, and has often been criticized for wasting computation time. To address these issues, the proposed approach is composed of the VNS with a modified 1 -opt local search, an extended adaptive local search scheme for optimizing multiple objectives, and the multi-objective genetic algorithm (GA). Unlike conventional local search, the proposed adaptive local search scheme automatically determines whether the VNS is used in a GA loop or not. We investigate the performance of the proposed approach in comparison to multi-objective GA-based approaches without local search and augmented with traditional local search. The computational results indicate that the proposed approach with adaptive VNS is more efficient in most of the performance measures and can find near-optimal layouts by optimizing multiple criteria simultaneously.
  
  PubDate: 2013-01-17T03:28:34Z
A parallel multi-objective optimization algorithm for the calibration of mathematical models
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  Based on evolutionary computation techniques, we present a parallel, globally convergent, multi-objective optimization algorithm which extends the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). This approach enables identifying multiple global optima and multiple discontinuous Pareto set solutions of the optimization problem in a compact search space. After evaluating the algorithm with test functions, we apply our method to the identification of the parameters of a reaction–diffusion model of a genetic regulatory mechanism during Drosophila early development, our simulations being in agreement with the experimental data. Comparisons with a multi-objective version of the CMA-ES (MO-CMA-ES) on this dataset show that our algorithm outperforms largely the speed of convergence of MO-CMA-ES. We have identified an infinite number of accurate solutions of the model equations, associated with the Pareto set of the optimization problem. This non-unicity property of a biological developmental process explains phenotypic plasticity and resilience in biological systems.
  
  PubDate: 2013-01-17T03:28:34Z
A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  In this paper, a hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems. The bilevel programming is transformed into a single level programming problem by use of the KKT conditions of the lower level problem. Then, the hybrid intelligent algorithm is proposed to solve the transformed problem. Our approach embeds the CST into PSO. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. Secondly, an optimization problem is solved by CST to judge whether the particle is feasible or not. In each iteration, all the feasible particles are ranked in ascending order. Particles in the front of list are updated by PSO, while particles in the end of list are updated by CST. The CST used here is not only to enhance the particles but also to improve the diversity of the particle swarm so as to avoid PSO trapping the local optima. Finally, the hybrid intelligent algorithm is commented by illustrating the numerical results on several benchmark problems from the references.
  
  PubDate: 2013-01-17T03:28:34Z
Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  The stand-alone energy system having a photovoltaic (PV) panels or wind turbines have low reliability and high cost as compared with wind/PV hybrid energy system. In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of wind/PV hybrid energy system in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of hybrid energy system, while guaranteeing the availability of energy. A diesel generator is added to ensure uninterrupted power supply due to the intermittent nature of wind and solar resources. Due to the complexity of the hybrid energy system design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/ PV hybrid energy systems to supply a located in the area of Jaipur, Rajasthan (India). Conventional methods require calculation at every single combination of sizing, operation strategy and the data for each variation of component needs to be entered manually and execute separately. Results show that the hybrid energy systems can deliver energy in a stand-alone installation with an acceptable cost. It is clear from the results that the proposed BBO method has excellent convergence property, require less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain the better solution.
  
  PubDate: 2013-01-17T03:28:34Z
Optimal test sequence generation using firefly algorithm
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.
  
  PubDate: 2013-01-17T03:28:34Z
Practical multi-objective controller for preventing noise and vibration in an automobile wiper system
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  This paper presents an approach using a multi-objective controller to prevent noise and vibration generated by the wiper blade during its wiping operation. Firstly, this paper focuses on the experimental approach to collect noise and vibration data from a car wiper system during its operation and secondly, to develop black box model of the wiper system using nonparametric approach of system identification known as nonlinear auto regressive exogenous Elman neural network (NARXENN). Finally, a novel closed loop iterative input shaping controller whose parameters are tuned simultaneously by a Pareto based multi objective genetic algorithm (MOGA) are proposed and simulated in such a way that it can prevent unwanted noise and vibration in the wiper system. The main contribution of this work rather the previous studies of automobile wiper system is to develop a novel multi-objective control strategy whereby an automobile wiper blade is moved within its sweep workspace in the least amount of time with minimum noise and vibration.
  
  PubDate: 2013-01-17T03:28:34Z
A hybrid optimization approach for PI controller tuning based on gain and phase margin specifications
- Abstract: February 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation, Volume 8
  
  Controllers designed for industrial processes should possess high robust performance to counter variations in system parameters. Gain margin and phase margin are two frequency domain specifications that assess the degree of robustness of a system. In this paper, a two-stage hybrid optimization algorithm has been developed for the tuning of proportional integral (PI) controller based on the above frequency domain specifications. Till date an accurate analytical tuning method for achieving specified gain margin and phase margin for a general class of systems is unavailable because of complex nonlinearities involved in the gain and phase margin equations. The proposed algorithm involves the combination of a stochastic population based optimization technique and a pattern search based (Hooke–Jeeves) method. The parameter tuning process has been framed as an optimization problem by employing an objective function based on equations corresponding to gain and phase margin specification. In the proposed algorithm, a global search is first carried out over the search space to determine an initial set of desired controller parameters using Particle Swarm Optimization (PSO). The search is then refined in the second stage using Hooke Jeeves method (initialized using the PSO solution). The proposed method is free of any numerical approximation which consequently improves the accuracy of final solution. For a given transfer function, computer simulations reveal that the hybrid approach meets the desired specifications with greater accuracy of final solution as compared to the existing techniques and other evolutionary algorithms.
  
  PubDate: 2013-01-17T03:28:34Z
Profit based unit commitment for GENCOs using Parallel NACO in a distributed cluster
- Abstract: Available online 16 January 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  Deregulation process has created an intense competition with the participation of many generating companies (GENCOs) in a power market. Wholesale transactions (bids and offer) have to be cleared and settled in a shorter duration. Therefore, this necessitates for the system operator to quick and smarter decisions. In this problem formulation, profit based unit commitment (PBUC) problem aims in maximizing the profit of GENCOs. However demand satisfaction is not an obligation. Here, parallel nodal ant colony optimization (PNACO) approach mimicking ant's intelligence is used in the decision on committing generating units. The sub problem economic dispatch (ED) is carried out using parallel artificial bee colony (PABC) approach mimicking foraging behavior of bees. Profit based unit commitment (PBUC) must be obtained in less time even though there is a possible increase in generating units. Nowadays, as computing resources are available in plenty, effective utilization will be advantageous for reducing the time complexity for a large scale power system solution. The proposed approach uses a cluster of computers performing parallel operations in a distributed environment for obtaining the PBUC solution. The time complexity and the solution quality with respect to the number of processors in the cluster are thoroughly investigated. The effectiveness of the proposed approach for PBUC is first validated on a standard 10 unit system available in the literature and then analysis for computational efficiency using 1000 generating units, which is a duplicate form of standard 10 unit system.
  
  PubDate: 2013-01-17T03:28:34Z
A Robust Clustering Method based on Blind, Naked Mole-Rats (BNMR) Algorithm
- Abstract: Available online 14 January 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  One of most popular data clustering algorithms is K-means algorithm that uses the distance criterion for measuring the correlation among data. To do that, we should know in advance the number of classes (K) and choose K data point as an initial set to run the algorithm. However, the choice of initial points is a main problem in this algorithm, which may cause that the algorithm converges to local optima. So, some other clustering algorithms have been proposed to overcome this problem such as the methods based on K-means (SBKM), Genetic Algorithm (GAPS and VGAPS), Particle Swarm Optimization (PSO), Ant Colony Optimization (Dynamic ants), Simulated Annealing (SA) and Artificial Bee Colony (ABC) algorithm. In this paper, we employ a new meta-heuristic algorithm. We called it blind, naked mole-rats (BNMR) algorithm, for data clustering. The algorithm was inspired by social behavior of the blind, naked mole-rats colony in searching the food and protecting the colony against invasions. We developed a new data clustering based on this algorithm, which has the advantages such as high speed of convergence. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained using other mentioned methods showed the better accuracy and high speed of the new algorithm.
  Highlights ► The paper proposes conceptually a novel method based on new optimization algorithm. ► BNMR algorithm increases the speed of its convergence.► BNMR algorithm used in our method not only prevents data clustering algorithm from being trapped in local optima. ► The results obtained by applying the proposed method on various datasets demonstrate the superiority of our method.
  
  PubDate: 2013-01-17T03:28:34Z
Memetic algorithms applied to the optimization of workflow compositions
- Abstract: Available online 11 January 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  The selection of services of a workflow based on Quality of Service (QoS) attributes is an important issue in service-oriented systems. QoS attributes allow for a better selection process based on non-functional quality criteria such as reliability, availability, and response time. Past research has mostly addressed this problem with optimal methods such as linear programming approaches. Given the nature of service-oriented systems where large numbers of services are available with different QoS values, optimal methods are not suitable and therefore, approximate techniques are necessary. In this paper, we investigate Genetic algorithms and Particle swarm optimization for the service selection process. In particular, both methods are combined with an optimal assignment algorithm (Munkres algorithm) in order to achieve higher solution qualities (success ratios) and to form a so called memetic algorithm. Experiments are conducted to investigate the suitability of the approaches and to compare the memetic algorithms with their non-memetic counterparts. The results reveal that the memetic algorithms are very suitable for the application to the workflow selection problem.
  
  PubDate: 2013-01-17T03:28:34Z
Distributed learning with biogeography-based optimization: Markov modeling and robot control
- Abstract: Available online 9 January 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  Biogeography-based optimization (BBO) is an evolutionary algorithm that is motivated by biogeography, which is the science that describes how biological species are geographically distributed. We extend the standard BBO algorithm to distributed learning, which does not require centralized coordination of the population. We call this new algorithm distributed BBO (DBBO). We derive a Markov model for DBBO, which provides an exact mathematical model of the DBBO population in the limit as the generation number approaches infinity. We use standard benchmark functions to compare BBO and DBBO with several other evolutionary optimization algorithms, and we show that BBO and DBBO give competitive results, especially for multimodal problems. Benchmark results show that DBBO performance is almost identical to BBO. We also demonstrate DBBO on a real-world application, which is the optimization of robot control algorithms, using both simulated and experimental mobile robots.
  
  PubDate: 2013-01-17T03:28:34Z
An improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration–exploitation balance
- Abstract: Available online 9 January 2013
  Publication year: 2013
  Source:Swarm and Evolutionary Computation
  
  Despite its relatively high convergence rate, the particle swarm optimization (PSO) algorithm is quite vulnerable to premature convergence to local minima. To tackle this problem an improved territorial particle swarm optimization (TPSO) algorithm is presented in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. A new “collision operator” and adaptively varying “territories” are used to prevent the particles from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric, and thus improves exploration ability. The collision operator is shown to provide the algorithm with the ability of controlling the diversity throughout the different stages of the search process. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors' best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively and thus provides the algorithm with a local search ability. The efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed benchmark functions presented in CEC2005 competition and the results are compared with recent variants of the original PSO and CMA-ES the winner of CEC2005 competition.
  
  PubDate: 2013-01-17T03:28:34Z
Multi-output majority gate-based design optimization by using evolutionary algorithm
- Abstract: Available online 20 December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  In this paper, a novel efficient method for optimizing multi-output majority gate based designs is proposed. Majority gate is a fundamental Boolean operator in some nano-scale technologies such as quantum-dot cellular automata (QCA). As a result, the design optimization must be directly implemented on majority gates instead of optimizing the design for AND–OR gates. In some other nanotechnologies, a fundamental element is Minority gate which could be simply converted to majority gate by the De Morgan's theorem. Here, the proposed optimization method works on the basis of evolutionary computation and can reduce both the number of majority gates and the worst-case delay of the circuit. The method is compared to some other optimization algorithms and its efficiency is verified.
  
  PubDate: 2013-01-17T03:28:34Z
Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network
- Abstract: Available online 10 December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable performance of the proposed method. In order to find the maximum exploration potentials, these techniques are equipped with an external archive. These archives aid the methods to record all of the non-dominated solutions. Eventually, the proposed method and generalized regression neural network (GRNN) are simultaneously used to optimize the major parameters of an irreversible thermal engine. In order to direct the PBMSB to explore deliberate spaces within the solution domain, a reference point obtained from finite time thermodynamic (FTT) approach, is utilized in the optimization. The outcome results show the acceptable performance of the proposed method to optimize complex real-life engineering systems.
  
  PubDate: 2013-01-17T03:28:34Z
Inside Front Cover: Editorial board
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  PubDate: 2013-01-17T03:28:34Z
Network decomposition using Kernighan–Lin strategy aided harmony search algorithm
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  Power system is a large-scale network with a number of components and interconnections for which centralized control becomes cumbersome. For multi-area computations, decentralization is necessary. For implementation of this approach network decomposition becomes an essential task. In this paper the network decomposition problem is solved as an optimization problem using the harmony search (HS) algorithm. To improve the performance of the HS algorithm, a widely used graph bi-partitioning method called Kernighan–Lin (KL) strategy is used in the improvisation process. KL strategy is used in the partitioning of digital and VLSI circuits and is suitable for bi-partitioning networks. The connectivity of the partitioned clusters are checked by means of graph traversal techniques. Simulation are carried out on IEEE Standard systems and found to be very effective in decomposition of the system hierarchically.
  Highlights ► Optimal Network Decomposition is done using Harmony Search Algorithm. ► The Objective is to balance the computational load and minimise the communication. ► Kernighan–Lin strategy is used to improve the harmony memory in the algorithm. ► It finds application in distributed computing of power system computations. ► Performance is validated using IEEE standard test systems.
  
  PubDate: 2013-01-17T03:28:34Z
Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  The design of cognitive radio engine aims at adapting the radio parameters to a predefined set of objective functions in communication system and may be formulated as a constrained multiobjective optimization problem. In the proposed work, an efficient design of orthogonal frequency division multiplexing based cognitive radio is carried out using multiobjective evolutionary algorithms. The performances of different algorithms are assessed and compared using three statistical metrics. The simulation results show that our proposed approach outperforms other algorithms while designing a cognitive radio engine. Our proposed approach which is based on the concept of cat swarm optimization, not only efficiently computes but also finds better nondominating solutions. In this paper, multiobjective evolutionary algorithms are applied to the parameter adaptation of a OFDM based cognitive radio engine. The spectral interference between primary and cognitive users is taken into consideration which plays a major role in communication. Due to heuristic nature of evolutionary algorithms, the stability of the simulation results is verified using different statistical tests. A fuzzy logic based strategy is shown in order to find out a compromised solution on the Pareto front.
  
  PubDate: 2013-01-17T03:28:34Z
Hybrid particle swarm optimization transplanted into a hyper-heuristic structure for solving examination timetabling problem
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  Examination timetabling is a discrete, multi-objective and combinatorial optimization problem which tends to be solved with a cooperation of stochastic search approaches such as evolutionary algorithms (EAs) and heuristic methods such as sequential graph coloring heuristics. This research investigates the use of discrete particle swarm optimization (DPSO) for solving examination timetabling problem. A combination of mutation, specialist recombination operator and graph coloring heuristics are used to update position of particles in the DPSO. A new local search method, called two staged hill climbing, is proposed and is utilized to hybridize the DPSO algorithm. Three structures for the DPSO and three strategies to hybridize it are proposed. On one hand, since the proposed DPSO algorithms such as hyper-heuristics methods employ a strategy to manage a set of constructive low-level heuristics, they can be classified as hyper-heuristic systems and, on the other hand, the DPSO is a stochastic global optimization method from class of EAs. The proposed algorithms are tested on a set of Carter benchmark problems to set the parameters of algorithms and also to compare different methods. The obtained results demonstrate that the proposed hill climbing local search, in spite of its simplicity, has a better performance than original hill climbing method. Among different graph coloring heuristics, those of algorithms which employ the saturation degree heuristic lead to the better results. Also among different proposed algorithms, the first structure of DPSO and third strategy of hybridizing obtain a better performance than the other structures and strategies. In a later part of the comparative experiment, performance comparisons of the proposed algorithms with some other hyper-heuristic and EA methods are done. The obtained results confirm that the proposed hybrid algorithm has a better, or at least comparable, performance than other hyper-heuristic systems. Also it obtains the best results among hyper-heuristic systems on some problems. Also in comparison of other EAs, it has a completely comparable performance.
  
  PubDate: 2013-01-17T03:28:34Z
A multi structure genetic algorithm for integrated design space exploration of scheduling and allocation in high level synthesis for DSP kernels
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  This paper presents an integrated design space exploration of scheduling and allocation problem in high level synthesis using the heuristic based multi structure genetic algorithm. A cost function based on a combination of power consumption and pipelined execution time as well as a scheme to select functional unit type in case of multiple versions is proposed that can guide the genetic algorithm to near-optimal/optimal solution. The cost function model takes the functional units, registers, multiplexers and demultiplexers into consideration. The encoding process of the parent chromosome incorporates a special seeding process that enables the genetic algorithm to search for an optimal solution. This type of seeding process was specifically incorporated because the optimal solution to a problem always lies between the maximum serial and parallel implementation. Therefore it is always capable of finding a near-optimal/optimal solution to the combined problem of scheduling and allocation based on the provided user specified constraints. Results of the comparison with another recent genetic algorithm based exploration technique indicated considerable reduction of execution clock cycle as well as power consumption for almost all the benchmarks.
  
  PubDate: 2013-01-17T03:28:34Z
Biogeography based optimization technique for best compromise solution of economic emission dispatch
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  This paper attempts to develop an elegant biogeography based optimization algorithm for obtaining the best compromise solution of EED problem. The basic philosophy orients to create a modified objective function that provides equal significance to both fuel cost and emission cost components. Besides it requires only three solution runs unlike other strategies involving more solution runs. The performance is evaluated through three test systems and the simulation results are presented to demonstrate the validity and effectiveness of the scheme for practical applications.
  Highlights ► An elegant optimization strategy is developed to elicit the best compromise solution for economic emission dispatch (EED) problem. ► It uses biogeography based optimization as a tool to solve the EED problem. ► This is a new approach in the sense the bi-objective function is modified to obtain the best compromise solution in three solution run. ► The results appear as a promising endeavor in its mission to serve as a tool for practical applications.
  
  PubDate: 2013-01-17T03:28:34Z
Craziness based Particle Swarm Optimization algorithm for FIR band stop filter design
- Abstract: December 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 7
  
  In this paper, an improved particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed and employed for digital finite impulse response (FIR) band stop filter design. The design of FIR filter is generally nonlinear and multimodal. Hence gradient based classical optimization methods are not suitable for digital filter design due to sub-optimality problem. So, global optimization techniques are required to avoid local minima problem. Several heuristic approaches are available in the literatures. The Particle Swarm Optimization (PSO) algorithm is a heuristic approach with two main advantages: it has fast convergence, and it uses only a few control parameters. But the performance of PSO depends on its parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems the PSO algorithm has been modified in this paper and is used for FIR filter design. In birds' flocking or fish schooling, a bird or a fish often changes directions suddenly. This is described by using a “craziness” factor and is modelled in the technique by using a craziness variable. A craziness operator is introduced in the proposed technique to ensure that the particle would have a predefined craziness probability to maintain the diversity of the particles. The algorithm's performance is studied with the comparison of real coded genetic algorithm (RGA), conventional PSO, comprehensive learning particle swarm optimization (CLPSO) and Parks and McClellan (PM) Algorithm. The simulation results show that the CRPSO is superior or comparable to the other algorithms for the employed examples and can be efficiently used for FIR filter design.
  
  PubDate: 2013-01-17T03:28:34Z
Adaptive evolutionary programming with p-best mutation strategy
- Abstract: Available online 29 November 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  Although initially conceived for evolving finite state machines, Evolutionary Programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization, EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we propose a p-best mutation scheme for EP where any one from the p ( p ∈ [ 1 , 2 , … , μ ] , where μ denotes population size) top-ranked population-members (according to fitness values) is selected randomly for mutation. The scheme is invoked with 50% probability with each index in the current population, i.e. the i-th offspring can now be obtained either by mutating the i-th parent or by mutating a randomly selected individual from the p top-ranked vectors. The percentage of best members is made dynamic by decreasing p in from μ / 2 to 1 with generations to favor explorative behavior at the early stages of search and exploitation during the later stages. We investigate the effectiveness of introducing controlled bias in parent selection in conjunction with an Adaptive Fast EP (AFEP), where the value of a strategy parameter is updated based on the previous records of successful mutations by the same parameter. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on Evolutionary Computation) 2005 test-suite for real-parameter optimization and two other engineering optimization problems reflects the statistically validated superiority of the new scheme in terms of final accuracy, speed, and robustness. Comparison with AFEP without p-best mutation demonstrates the improvement of performance due to the proposed mutation scheme alone.
  
  PubDate: 2013-01-17T03:28:34Z
Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem
- Abstract: Available online 23 November 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  This paper presents a model for maintenance scheduling (MS) of generators using hybrid improved binary particle swarm optimization (IBPSO) based coordinated deterministic and stochastic approach. The objective function of this paper is to reduce the loss of load probability (LOLP) and minimizing the annual supply reserve ratio deviation for a power system which are considered as a measure of power system reliability. Genetic algorithm (GA) operators are introduced in the IBPSO to acquire diversified solutions in the search space. Moreover, in this paper, the hybrid IBPSO based economic dispatch (ED) has been decomposed as a sub-problem in the maintenance model that results to a more practical maintenance schedule. A case study for the real power system model in Odisha (India) is considered. Comprehensive studies have also been carried out for the different power system consisting of 5-unit system, 21-unit system and IEEE reliability test system (RTS). It shows that the proposed algorithm can accomplish a significant levelization in the reliability indices over the planning horizon for reliable operation of the power system and demonstrates the usefulness of the proposed approach. The proposed method yields better result by means of improved search performance and better convergence characteristics which are compared to the other optimization methods and conventional method.
  Highlights ► We have presented hybrid improved binary particle swarm optimization based maintenance scheduling. ► Forced random outages of generators are considered in the proposed algorithm. ► Minimizations of loss of load probability, annual supply reserve ratio deviation are two major criteria. ► GA operators are introduced in IBPSO to acquire diversified solutions. ► Hybrid IBPSO based economic dispatch has been decomposed as a sub-problem.
  
  PubDate: 2013-01-17T03:28:34Z
Distributed DOA estimation using clustering of sensor nodes and diffusion PSO algorithm
- Abstract: Available online 23 November 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  This paper proposes a distributed DOA estimation technique using clustering of sensor nodes and distributed PSO algorithm. The sensor nodes are suited by clustered to act as random arrays. Each cluster estimates the source bearing by optimizing the Maximum Likelihood (ML) function locally with cooperation of other clusters. During the estimation process each cluster shares its best information obtained by Diffusion Particle Swarm Optimization (DPSO) with other clusters so that the global estimation is achieved. The performance of the proposed technique has been evaluated through simulation study and is compared with that of obtained by the centralized and decentralized MUltiple SIgnal Classification (MUSIC) algorithms and distributed in-network algorithm. The results demonstrate improved performance of the proposed method compared to others. However, the new method exhibits slightly inferior performance compared to the centralized Particle Swarm Optimization-Maximum Likelihood (PSO-ML) algorithm. Further the proposed method offers low communication overheads compared to other methods.
  
  PubDate: 2013-01-17T03:28:34Z
Empirical investigations into the exponential crossover of differential evolutions
- Abstract: Available online 29 October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  Since the introduction in 1995, Differential evolution (DE) has drawn the attention of many researchers all over the world. Even though binomial (uniform) and exponential (modular two-point) crossover operators have been proposed simultaneously, binomial crossover has been more frequently used. Recently, the exponential crossover operator demonstrated superior performance over the binomial operator when solving complex high dimensional problems. We observe that the commonly used definition of exponential crossover operator does not scale impractically with the dimensionality of the problem being solved. Motivated by these observations, in this research, we investigate the performance of the current exponential crossover operator (EXP) and demonstrate its deficiencies. Consequently, a linearly scalable exponential crossover operator (LS-EXP) based on a number of consecutive dimensions to crossover is defined. Our numerical results on the most recent benchmark problems with dimensions ranging from 50 to 1000 show superior performance of the LS-EXP over the current exponential crossover operator, EXP.
  
  PubDate: 2013-01-17T03:28:34Z
Exploring isospectral cantilever beams using electromagnetism inspired optimization technique
- Abstract: Available online 16 October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  Isospectral beams have identical free vibration frequency spectrum for a specific boundary condition. The problem of finding non-uniform beams which are isospectral to a given uniform beam, with fixed-free boundary condition, leads to a multimodal optimization problem. The first Q natural frequencies of the given uniform Euler–Bernoulli beam are determined using analytical solution. The first Q natural frequencies of a non-uniform beam are obtained with the help of finite element modeling. In order to obtain the non-uniform beams isospectral to a given uniform beam, an error function is designed, which calculates the difference between the spectra of the given uniform beam and the non-uniform beam. In our study, this error function is minimized using electromagnetism inspired optimization technique, a population based iterative algorithm inspired by the attraction–repulsion physics of electromagnetism. Numerical results show the existence of the isospectral non-uniform beams for a given uniform beam, which occur as local minima. Non-uniform beams isospectral to a damaged beam, are also explored using the proposed methodology to illustrate the fact that accurate structural damage identification is difficult by just frequency measurements.
  
  PubDate: 2013-01-17T03:28:34Z
Feature subset selection using differential evolution and a wheel based search strategy
- Abstract: Available online 5 October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  Differential evolution has started to attract a lot of attention as a powerful search method and has been successfully applied to a variety of applications including pattern recognition. One of the most important tasks in many pattern recognition systems is to find an informative subset of features that can effectively represent the underlying problem. Specifically, a large number of features can affect the system's classification accuracy and learning time. In order to overcome such problems, we propose a new feature selection method that utilizes differential evolution in a novel manner to identify relevant feature subsets. The proposed method aims to reduce the search space using a simple, yet powerful, procedure that involves distributing the features among a set of wheels. Two versions of the method are presented. In the first one, the desired feature subset size is predefined by the user, while in the second the user only needs to set an upper limit to the feature subset size. Experiments on a number of datasets with different sizes proved that the proposed method can achieve remarkably good results when compared with some of the well-known feature selection methods.
  
  PubDate: 2013-01-17T03:28:34Z
Inside Front Cover: Editorial board
- Abstract: October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 6
  
  PubDate: 2013-01-17T03:28:34Z
Evolutionary dynamic optimization: A survey of the state of the art
- Abstract: October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 6
  
  Optimization in dynamic environments is a challenging but important task since many real-world optimization problems are changing over time. Evolutionary computation and swarm intelligence are good tools to address optimization problems in dynamic environments due to their inspiration from natural self-organized systems and biological evolution, which have always been subject to changing environments. Evolutionary optimization in dynamic environments, or evolutionary dynamic optimization (EDO), has attracted a lot of research effort during the last 20 years, and has become one of the most active research areas in the field of evolutionary computation. In this paper we carry out an in-depth survey of the state-of-the-art of academic research in the field of EDO and other meta-heuristics in four areas: benchmark problems/generators, performance measures, algorithmic approaches, and theoretical studies. The purpose is to for the first time (i) provide detailed explanations of how current approaches work; (ii) review the strengths and weaknesses of each approach; (iii) discuss the current assumptions and coverage of existing EDO research; and (iv) identify current gaps, challenges and opportunities in EDO.
  
  PubDate: 2013-01-17T03:28:34Z
Optimum load shedding based on sensitivity to enhance static voltage stability using DE
- Abstract: October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 6
  
  This paper proposes a methodology to optimize the load curtailments necessary to restore the equilibrium of operating point by accounting for operating and stability inequality constraints. To get desired stability margin Schur's inequality based proximity indicator has been selected whose threshold value along with minimization of load shedding assures desired static voltage stability margin. The methodology anticipates the risk of voltage instability in a time frame using sensitivity of proximity indicator of load flow Jacobian with respect to load. If the normal controls are exhausted, the proposed algorithm based on sensitivity, sheds, required amount of low priority loads in advance. This makes the system to survive voltage instability threat even during worst system period. The buses which are having large sensitivity are selected for load shedding. A computational algorithm for minimum load shedding at selected load buses has been developed using Differential Evolution (DE), Self-adaptive Differential Evolution (SaDE) and Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE). Developed algorithm accounts inequality constraints not only in present operating conditions (after load shedding) but also for predicted next interval load (with load shedding). Proposed methodology has been implemented on IEEE 14-bus and 25-bus test systems. Performance of the methodology has been compared with Davidon–Fletcher–Powell's (DFP), Particle Swarm Optimization (PSO), Co-ordinated Aggregation based Particle Swarm Optimization (CAPSO) and Genetic Algorithm (GA) techniques based on statistical inference. Simulation results have been obtained which confirm that the proposed methodology provide considerable mitigation in the load shedding and enhancement in voltage stability. By using this methodology various power system blackouts can be prevented.
  Highlights ► A new search algorithm is proposed for anticipatory load shedding under emergency. ► The proposed algorithm is based on sensitivity and voltage stability considerations. ► A computational algorithm for minimum load shed is developed using DE and its improved variants. ► Inequality constraints are accounted not only in present operating condition but also for predicted next interval. ► The proposed algorithm has been implemented on IEEE 14-bus and 25-bus systems with promising results.
  
  PubDate: 2013-01-17T03:28:34Z
A new approach to multi-aircraft air combat assignments
- Abstract: October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 6
  
  In this paper, a multi-layer perceptron based on 6 important features is proposed for predicting the time needed by our aircraft to approach enemy aircraft when they are in a combat situation. Then the prediction module is incorporated into a SOMO-based decision aid for solving multi-aircraft assignment problems. The problem of assigning our aircraft to meet head-on enemy aircraft in a combat situation is formulated as a constrained resource allocation problem. The objective function of the multi-aircraft assignment problem is a measure for computing the expected overall predominance value of aircraft over enemy aircraft based on four predominance factors. The SOMO algorithm which is an extension version of the SOM algorithm for optimization is executed to find an assignment such that the expected overall predominance value of aircraft can be maximized. Four hundred combat situations are simulated to test the effectiveness of the proposed SOMO-based decision aid. The simulation results demonstrate that the SOMO-based decision aid could provide appealing solutions within 0.5 s even for combat situations with a large number of aircraft.
  
  PubDate: 2013-01-17T03:28:34Z
A combined approach for clustering based on K-means and gravitational search algorithms
- Abstract: October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 6
  
  Clustering is an attractive and important task in data mining that is used in many applications. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The gravitational search algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this paper, we present a hybrid data clustering algorithm based on GSA and k-means (GSA-KM), which uses the advantages of both algorithms. The GSA-KM algorithm helps the k-means algorithm to escape from local optima and also increases the convergence speed of the GSA algorithm. We compared the performance of GSA-KM with other well-known algorithms, including k-means, genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), honey bee mating optimization (HBMO), particle swarm optimization (PSO) and gravitational search algorithm (GSA). Five real and standard datasets from the UCI repository have been used to demonstrate the results of the algorithms. The experimental results are encouraging in terms of the quality of the solutions and the convergence speed of the proposed algorithm.
  
  PubDate: 2013-01-17T03:28:34Z
S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization
- Abstract: Available online 1 October 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation
  
  Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a transfer function which is responsible to map a continuous search space to a discrete search space. Currently there appears to be insufficient focus on the transfer function in the literature despite its apparent importance. In this study six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated. Twenty-five benchmark optimization functions provided by CEC 2005 special session are employed to evaluate these transfer functions and select the best one in terms of avoiding local minima and convergence speed. In order to validate the performance of the best transfer function, a comparative study with six recent modifications of BPSO is provided as well. The results prove that the new introduced v-shaped family of transfer functions significantly improves the performance of the original binary PSO.
  
  PubDate: 2013-01-17T03:28:34Z
Editorial Board
- Abstract: August 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 5
  
  PubDate: 2013-01-17T03:28:34Z
Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm
- Abstract: August 2012
  Publication year: 2012
  Source:Swarm and Evolutionary Computation, Volume 5
  
  This article proposes a hybrid cuckoo search algorithm (CSA) integrated with fuzzy system for solving multi-objective unit commitment problem (MOUCP). The power system stresses the need for economic, non-polluting and reliable operation. Hence three conflicting functions such as fuel cost, emission and reliability level of the system are considered. CSA mimics the breeding behavior of cuckoos, where each individual searches the most suitable nest to lay an egg (compromise solution) in order to maximize the egg’s survival rate and achieve the best habitat society. Fuzzy set theory is used to create the fuzzy membership search domain where it consists of all possible compromise solutions. CSA searches the best compromise solution within the fuzzy search domain simultaneously tuning the fuzzy design boundary variables. Tuning of fuzzy design variables eliminate the requirement of expertise needed for setting these variables. On solving MOUCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the real coded CSA solves economic dispatch problem (EDP) and also tunes the fuzzy design boundary variables. The proposed methodology is tested and validated for both the single and multi-objective optimization problems. The effectiveness of the proposed technique is demonstrated on 6, 10, 26 and 40 unit test systems by comparing its performance with other methods reported in the literature.
  
  PubDate: 2013-01-17T03:28:34Z