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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


Papers
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Journal ArticleDOI
TL;DR: The experimental results show that the HPSO is robust against different problem size, task interaction density, and network topology, and the proposed method is also more effective and efficient than a genetic algorithm for the test-cases studied.

105 citations

Proceedings ArticleDOI
Thomas A. Runkler1, C. Katz1
11 Sep 2006
TL;DR: Two new methods for minimizing the two reformulated versions of the FCM objective function by particle swarm optimization (PSO) by comparing them with alternating optimization (AO) and ant colony optimization (ACO) on two benchmark data sets.
Abstract: This paper deals with fuzzy clustering by minimizing the fuzzy c-means (FCM) model. We introduce two new methods for minimizing the two reformulated versions of the FCM objective function by particle swarm optimization (PSO). In PSO-V each particle represents a component of a cluster center. In PSO-U each particle represents an unsealed and unnormalized membership value. PSO-V and PSO-U are compared with alternating optimization (AO) and with ant colony optimization (ACO) on two benchmark data sets: the single outlier and the lung cancer data sets. The stochastic methods ACO, PSO-V, and PSO-U are slower than AO, but in each experiment one of the two PSO variants significantly outperforms the other algorithms.

105 citations

Journal ArticleDOI
Zhao Bo1, Cao Yi-jia1
TL;DR: Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objectives economic load dispatch.
Abstract: A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

105 citations

Journal ArticleDOI
01 Jun 2012
TL;DR: A new approach hybridizing PSO with bottleneck heuristic to fully exploit the bottleneck stage, and with simulated annealing to help escape from local optima to solve the HFS problem.
Abstract: Hybrid flow shops (HFS) are common manufacturing environments in many industries, such as the glass, steel, paper and textile industries. In this paper, we present a particle swarm optimization (PSO) algorithm for the HFS scheduling problem with minimum makespan objective. The main contribution of this paper is to develop a new approach hybridizing PSO with bottleneck heuristic to fully exploit the bottleneck stage, and with simulated annealing to help escape from local optima. The proposed PSO algorithm is tested on the benchmark problems provided by Carlier and Neron. Experimental results show that the proposed algorithm outperforms all the compared algorithms in solving the HFS problem.

105 citations

Journal ArticleDOI
TL;DR: FESPSO, a new fitness estimation strategy, is proposed for particle swarm optimization to reduce the number of fitness evaluations, thereby reducing the computational cost.

105 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171