Topic
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.
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TL;DR: The PSOStr outperformed the other methods in terms of both solution accuracy and computational cost (i.e. function evaluations), and is a hybrid of the evolutionary StrGA and the socially inspired PSOA.
101 citations
101 citations
23 May 2011
TL;DR: Experimental results based on a set of benchmark functions show that the proposed memetic algorithm is a good optimizer in multimodal optimization domain.
Abstract: In this paper, a new memetic algorithm, which combines PSO and local search technique, is proposed for mul-timodal optimization problems. In the investigated algorithm, a local PSO model is used to disperse the individuals into different sub-regions, an adaptive local search method is employed to refine the quality of individuals and a triggered re-initialization scheme is introduced to enhance the algorithm's capacity of solving functions with numerous optima. Experimental results based on a set of benchmark functions show that the proposed memetic algorithm is a good optimizer in multimodal optimization domain.
101 citations
TL;DR: Genetic algorithms, evolution strategies, and particle swarm optimization when applied to the classical capacitated vehicle routing problem and many of its variants and the best alternative problem-solving approaches on benchmark instances are reported on.
Abstract: This paper is a survey of the literature on applications of evolutionary algorithms for vehicle routing problems. It reports on genetic algorithms, evolution strategies, and particle swarm optimization when applied to the classical capacitated vehicle routing problem and many of its variants. The performance of evolutionary algorithms is also compared with the best alternative problem-solving approaches on benchmark instances.
101 citations
TL;DR: An example-based learning PSO (ELPSO) is proposed to overcome shortcomings of the canonical PSO by keeping a balance between swarm diversity and convergence speed and outperforms all the tested PSO algorithms in terms of both solution quality and convergence time.
101 citations