Particle swarm optimization with voronoi neighborhood

Particle swarm optimization is an optimization method based on a simulated social behavior displayed by artificial particles in a swarm, inspired from bird flocks and fish schools. An underlying component that influences the exchange of information between particles in a swarm, is its topological structure. Therefore, this property has a great influence on the comportment of the optimization method. In this study, we propose DCluster: a dynamic topology, based on a combination of two well-known topologies viz. Four-cluster and Fitness. The proposed topology is analyzed, and compared to six other topologies used in the standard PSO algorithm using a set of benchmark test functions and several well-known constrained and unconstrained engineering design problems. Our comparisons demonstrate that DCluster outperforms the other tested topologies and leads to satisfactory performance while avoiding the problem of premature convergence.

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A grid-guided particle swarm optimizer for multimodal multi-objective problems

Yan

et al. 2022

Applied Soft Computing

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Particle swarm optimization with voronoi neighborhood

Cited by 3 publications

References 11 publications

A Comparison Study of PSO Neighborhoods

A Comparison Study of PSO Neighborhoods

Dynamic cluster in particle swarm optimization algorithm

A grid-guided particle swarm optimizer for multimodal multi-objective problems

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