Genetic Learning Particle Swarm Optimization with Diverse Selection
Particle swarm optimization (PSO) is a widely used heuristic algorithm. However, canonical PSO may lead to premature convergence. To solve this problem, researchers try to hybridize PSO with genetic algorithm (GA) which facilitates global effectiveness. One of the successful algorithms is genetic learning PSO (GL-PSO). However, we find that the selection in GL-PSO reduce the diversity of particles. It may lead premature convergence in some test functions. To solve this problem, we figure out a genetic learning particle swarm optimization with diverse selection (GL-PSODS). We test our proposed algorithm in test functions of CEC2014. Our experiments show that GL-PSODS has an improvement in some test functions compared to PSO and GL-PSO.
KeywordsParticle swarm optimization Genetic algorithm GL-PSO
This work is supported by the Fundamental Research Funds for the Central Universities, SCUT (NO. 2017ZD048,2015ZM136), Tiptop Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2015TQ01X633), Science and Technology Planning Project of Guangdong Province, China (No. 2016A030310423), Science and Technology Program of Guangzhou (International Science and Technology Cooperation Program No. 201704030076) and Science and Technology Planning Major Project of Guangdong Province (No. 2015A070711001).
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