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DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch101
Pages 629-638
First published 30 July 2014

Hierarchical Heterogeneous Particle Swarm Optimization

Xinpei Ma and Hiroki Sayama

Abstract (Excerpt)

Particle swarm optimization (PSO) has recently been modified to several versions. Heterogeneous PSO is a recent extension which includes behavioral heterogeneity of particles. Here we propose a further developed version that has hierarchical interaction patterns among heterogeneous particles, which we call hierarchical heterogeneous PSO (HHPSO). Two algorithm designs that have been developed and tested are multi-layer HHPSO (ml-HHPSO) and multi-group HHPSO (mg-HHPSO). The performances of these algorithms were measured on a set of benchmark functions and compared with standard PSO and heterogeneous PSO. The results showed that the performances of both HHPSO algorithms were significantly improved from standard PSO and heterogeneous PSO, with higher quality of optimal solutions and faster convergence speed.