Particle swarm optimization based on deindividuation theory
YU Fe · Kongzhi yu juece · 2013
When resolving complex problems,the particle swarm optimization(PSO) has some disadvantages of slow convergence rate and easiness to fall into local optimal solution.Therefore,a particle swarm optimization with the deindividuation theory(DTPSO) is proposed.Based on the social identity model of deindividuation effects,the diversity and effectiveness of population can be maintained through the deindividuation acts,the balance between individuality and convergence) of individual particles during their evolution process.The results of simulation experiment show that the DTPSO possesses higher convergence rate and convergence precision,as well as better stability.