Convergence Analysis of a Class of Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithms and Its Application
SHI Zhan1,2, CHEN Qingwei1, HU Weili1
1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China; 2. Postdoctoral Programme, 28th Research Institute of China Electronics Technology Group Corporation, Nanjing, 210007, China
Abstract：For the drawback of ε-dominance, a new dominance relationship, ε-superior dominance, is proposed to solve the problem of easy loss of boundary point of Pareto optimal front. An overall framework for a class of multi-objective quantum-behaved particle swarm optimization (CMOQPSO) algorithms is constructed with the preserving strategy of optimal particle based on ε -superior dominance, and the global convergence of this class of algorithms is analyzed under certain conditions. A multi-objective quantum-behaved particle swarm optimization algorithm under the overall framework is applied to solving the problem of power transmission network planning, and the results denote that this class of CMOQPSO algorithms have good ability of global optimization.
施展, 陈庆伟, 胡维礼. 一类多目标量子行为粒子群优化算法收敛性分析及应用[J]. 信息与控制, 2013, 42(4): 407-415.
SHI Zhan, CHEN Qingwei, HU Weili. Convergence Analysis of a Class of Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithms and Its Application. Information and control, 2013, 42(4): 407-415.
