차분진화알고리듬과 혼합된 개체집단최적화 방법 연구

Particle Swarm Optimization (PSO) is one of the function evaluation based global optimization methods. In the framework of PSO, the swarm of particles that represent design variable values is intelligently moved toward the optimum solution. However, PSO often gets trapped in local optima because of lack of diversity of swarm members. The paper aims to implement the hybrid of PSO and Differential Evolution Algorithm (DEA), a hybrid particle swarm optimization (HPSO). The objective of the present study is to enhance the PSO performance incorporating with DEA. A number of mathematical function problems are explored to support the proposed method. The results show that HPSO converges to global optima faster than other global optimization methods such as PSO, DEA and a traditional version of Genetic Algorithm(GA).