Particle swarm optimization algorithm based on velocity differential mutation

Authors:
Shanhe Jiang;Qishen Wang;Julang Jiang
Affiliations:
Department of Physics and Power Engineering, Anqing Normal College, Anqing, China;Department of Physics and Power Engineering, Anqing Normal College, Anqing, China;Department of Physics and Power Engineering, Anqing Normal College, Anqing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 5
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Abstract

To deal with the problem of premature local convergence, slow search speed and low convergence accuracy in the late evolutionary, this paper proposes a particle swarm optimization algorithm based on velocity differential mutation (VDMPSO). Firstly, The cause of local convergence in the basic PSO algorithm is elaborated. Secondly, strategies of direct mutation for the particle velocity rather than the traditional particle position with differential evolution algorithm based on analying the relations of the particle velocity and the population diversity is introduced to improve the ability of effectively breaking away from the local optimum. By adding the mutation operation to the basic PSO algorithm, the proposed algorithm can maintain the characteristic of fast speed. Finally, the signficant performances in quality of the optimal solutions, the global search ability and convergence speed of algorithm proposed in this paper are validated by optimizing four benchmark functions.