应用改进的粒子群算法解决多阶段组合优化问题.pdfVIP

Expert Systems with Applications 38 (2011) 6727–6735 Contents lists available at ScienceDirect Expert Systems with Applications journal homepag e: /locate/eswa Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization Jun Sun a,⇑, Wei Fang a, Xiaojun Wu a, Choi-Hong Lai b, Wenbo Xu a a School of Information Technology, Jiangnan University, No. 1800, Lihu Avenue, Wuxi, Jiangsu 214122, China b School of Computing and Mathematical Sciences, University of Greenwich, Greenwich, London SE10 9LS, UK a r t i c l e i n f o a b s t r a c t Keywords: Solving the multi-stage portfolio optimization (MSPO) problem is very challenging due to nonlinearity of Risk management the problem and its high consumption of computational time. Many heuristic methods have been Multi-stage portfolio optimization employed to tackle the problem. In this paper, we propose a novel variant of particle swarm optimization Stochastic programming (PSO), called drift particle swarm optimization (DPSO), and apply it to the MSPO problem solving. The Heuristic methods classical return-variance function is employed as the objective function, and experiments on the prob- Particle swarm optimization lems with different numbers of stages are conducted by using sample data from various stocks in SP 100 index. We compare performance and effectiveness of DPSO, particle swarm optimization (PSO),