Particle swarm optimizer （PSO） is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimum when solving com- plex multimodal nonseparable problems. This paper p...Particle swarm optimizer （PSO） is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimum when solving com- plex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer （DLPSO） to solve multimodal nonseparable prob- lems. The strategy for DLPSO is to extract good vector infor- mation from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimen- tal studies on a set of test functions show that DLPSO ex- hibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.展开更多
基金国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2002CB3122000)国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.20060104Z1081)+2 种基金上海市自然科学基金(the Natural Science Foundation of Shanghai of China under Grant No.05ZR14038)国家杰出青年科学基金(the National Science Fund of China for Distinguished Young Scholar under Grant No.60625302)上海市科委重大基础研究基金(No.05DJ14002)。
文摘Particle swarm optimizer （PSO） is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimum when solving com- plex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer （DLPSO） to solve multimodal nonseparable prob- lems. The strategy for DLPSO is to extract good vector infor- mation from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimen- tal studies on a set of test functions show that DLPSO ex- hibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.
