Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization》.pdf

Computers Operations Research 60 (2015) 91–110 Contents lists available at ScienceDirect Computers Operations Research journal homepage: /locate/caor Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization Quande Qin a,b,c,d, Shi Cheng e,f,n, Qingyu Zhang a,b, Yiming Wei c,d, Yuhui Shi g a Department of Management Science, Shenzhen University, Shenzhen, China b Research Institute of Business Analytics and Supply Chain Management, Shenzhen, China c School of Management and Economics, Beijing Institute of Technology, Beijing, China d Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China e Division of Computer Science, The University of Nottingham Ningbo, China f International Doctoral Innovation Centre, The University of Nottingham Ningbo, China g Department of Electrical Electronic Engineering, Xi an Jiaotong-Liverpool University, Suzhou, China a r t i c l e i n f o a b s t r a c t Available online 25 February 2015 In the canonical particle swarm optimization (PSO), each particle updates its velocity and position by taking its Keywords: historical best experience and its neighbors best experience as exemplars and adding them together. Its Global optimization performance is largely dependent on the employed exemplars. However, this learning strategy in the canonical Learning strategy PSO is inefficient when complex problems are being optimized. In this paper, Multiple Strategies based Opposition-based learning