Modified Local Neighborhood Based Niching Particle Swarm Optimization for Multimodal Function Optimization
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A particle swarm optimization model for tracking multiple peaks over a multimodal fitness landscape is described here. Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space. Niching algorithms have the ability to locate and maintain more than one solution to a multi-modal optimization problem. The Particle Swarm Optimization (PSO) has remained an attractive alternative for solving complex and difficult optimization problems since its advent in 1995. However, both experiments and analysis show that the basic PSO algorithms cannot identify different optima, either global or local, and thus are not appropriate for multimodal optimization problems that require the location of multiple optima. In this paper a niching algorithm named as Modified Local Neighborhood Based Niching Particle Swarm Optimization (ML-NichePSO)is proposed. The ability, efficiency and usefulness of the proposed method to identify multiple optima are demonstrated using well-known numerical benchmarks.
KeywordsEvolutionary computation Swarm Intelligence Multimodal optimization Niching algorithms Particle Swarm Optimization Crowding
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