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A novel particle swarm optimizer hybridized with extremal optimization

Min-Rong Chen(optmrchen***at***gmail.com)

Abstract: Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO-EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. We testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other meta-heuristics. The proposed approach is shown to have superior performance and great capability of preventing premature convergence across it comparing favorably with the other algorithms.

Keywords: Particle swarm optimization;Extremal optimization; Numerical optimization; Meta-heuristics;Multimodal functions

Category 1: Global Optimization

Category 2: Combinatorial Optimization (Approximation Algorithms )

Citation: Submitted to Applied Soft Computing

Download: [PDF]

Entry Submitted: 05/26/2008
Entry Accepted: 05/30/2008
Entry Last Modified: 05/26/2008

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