Differential genetic particle swarm optimization for continuous function optimization

Authors:
Li Jian
Affiliations:
Department of Computer Engineering, Hubei University of Education, Wuhan, China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

In this paper, to introduce consistency and diversity, the concept of inertia weight is introduced to a modified genetic particle swarm optimization which was derived from the genetic particle swarm optimization (GPSO) and the differential evolution (DE). The proposed differential genetic particle swarm optimization (DGPSO) is implemented to thirteen well-known constrained optimization functions. And the simulation results have shown the feasibility and effectiveness. Moreover, DGPSO is employed to solve a tension/compression string design problem, and by comparison with the other methods, DGPSO has provided better results.