Distributed learning particle swarm optimizer for global optimization of multimodal problems

作     者：Geng ZHANG Yangmin LI Yuhui SHI 

作者机构：Department of Electromechanical Engineering Faculty of Science and Technology University of Macao Macao China Industrial and Systems Engineering Faculty of Engineering The Hong Kong Polytechnic University Hong Kong China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2018年第12卷第1期

页      面：122-134页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：support of the National Natural Science Foundation of China Macao Science and Technology Development Fund Research Committee of University of Macau 

主　　题：particle swarm optimizer (PSO) orthogonal ex-perimental design (OED) swarm intelligence 

摘      要：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.