Combinatorial particle swarm optimization (CPSO) for partitional clustering problem

Jarboui, Bassem; Cheikh, Mohamed; Siarry, Patrick; Rebaï, Ahmed

doi:10.1016/j.amc.2007.03.010

Cited by 100 publications

(31 citation statements)

References 6 publications

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“…From Fig. 4 and Tables 12,13,14,15,16,17 and 18 it can be seen that the DPSO-TOP algorithm is superior to the CGW and GLS algorithm for the 2, 3 and 4-member TOP. It is approximately at par in performance with the TMH algorithm.…”

Section: Comparison Of Dpso-top Algorithm With Other Heuristicsmentioning

confidence: 93%

“…DPSO is more commonly used for solving combinatorial optimization problems. Some of the optimization problems solved using DPSO include traveling salesman problem [32], vehicle routing problem [22], scheduling problem [1,[14][15][16][19][20][21]28,29], orienteering problem [8,9,24], clustering problem [13], and transmission network expansion problem [16].…”

Section: Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Discrete particle swarm optimization for the team orienteering problem

Muthuswamy

Lam

2011

Memetic Comp.

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Orienteering problem is a well researched routing problem which is a generalization of the traveling salesman problem. Team orienteering problem (TOP) is the extended version of the orienteering problem with more than one member in the team. In this paper the first known discrete particle swarm optimization (DPSO) algorithm has been developed for 2, 3 and 4-member TOP. In the DPSO meta-heuristic novel methods have been introduced for the initial particle generation process. Reduced variable neighborhood search and 2-opt were applied as the local search tools. The efficacy of the algorithm was tested using seven commonly used benchmark problem sets ranging in size from 21 to 102 nodes. The results of the DPSO algorithm were compared against seven other heuristic algorithms that have been developed for TOP. It was concluded that the developed DPSO algorithm for the TOP is competitive and robust across the benchmark problem sets.

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Section: Comparison Of Dpso-top Algorithm With Other Heuristicsmentioning

confidence: 93%

Section: Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Discrete particle swarm optimization for the team orienteering problem

Muthuswamy

Lam

2011

Memetic Comp.

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“…Recently, several successful applications were proposed in discrete optimization such as the CPSO algorithm for clustering problem [26], flowshop scheduling problem [17] and resources constrained project scheduling problem [27]. In [28] and [19] a discrete particle swarm optimization algorithm (DPSO) was proposed for solving permutation flowshop scheduling problem and no-wait flowshop scheduling problem respectively.…”

Section: Combinatorial Particle Swarm Optimization Algorithm (Cpso) Fmentioning

confidence: 99%

Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem

Eddaly

Jarboui

Siarry

2016

Journal of Computational Design and Engineering

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This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behaviour is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performace of the proposed approach is evaluated accroding to a real-world industrial problem.

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“…In Binary PSO, as the name suggests, the probability of searching space is either zero or one (Kennedy and Eberhart 1997). Optimization of hybrid problems which consists of continuous and integer variables is done using Combinatorial PSO (CPSO) (Jarbouia et al 2007). To solve constrained single objective problem, Constrained Optimization by PSO (COPSO) algorithm is used.…”

Section: Pso Variantsmentioning

confidence: 99%

A review on particle swarm optimization algorithms and their applications to data clustering

2010

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Data clustering is one of the most popular techniques in data mining. It is a method of grouping data into clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The most popular clustering algorithm K-mean and other classical algorithms suffer from disadvantages of initial centroid selection, local optima, low convergence rate problem etc. Particle Swarm Optimization (PSO) is a population based globalized search algorithm that mimics the capability (cognitive and social behavior) of swarms. PSO produces better results in complicated and multi-peak problems.

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Combinatorial particle swarm optimization (CPSO) for partitional clustering problem

Cited by 100 publications

References 6 publications

Discrete particle swarm optimization for the team orienteering problem

Discrete particle swarm optimization for the team orienteering problem

Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem

A review on particle swarm optimization algorithms and their applications to data clustering

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