Particle swarm optimization with selective particle regeneration for data clustering
► We present a novel algorithm developed based on particle swarm optimization. ► The algorithm contains particle regeneration operation. ► We apply this algorithm to data clustering problems. ► The proposed algorithm performs very well in the conducted numerical experiment. This paper presents selec...
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| Vydané v: | Expert systems with applications Ročník 38; číslo 6; s. 6565 - 6576 |
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| Jazyk: | English |
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Elsevier Ltd
01.06.2011
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | ► We present a novel algorithm developed based on particle swarm optimization. ► The algorithm contains particle regeneration operation. ► We apply this algorithm to data clustering problems. ► The proposed algorithm performs very well in the conducted numerical experiment.
This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems. |
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| AbstractList | ► We present a novel algorithm developed based on particle swarm optimization. ► The algorithm contains particle regeneration operation. ► We apply this algorithm to data clustering problems. ► The proposed algorithm performs very well in the conducted numerical experiment.
This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems. This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems. |
| Author | Tsai, Chi-Yang Kao, I-Wei |
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