信息与控制2011，Vol.40Issue(2)：214-220,226,8.DOI:10.3724/SP.J.1219.2010.00214
基于量子行为特性粒子群和自适应网格的多目标优化算法
Multi-Objective Optimization Algorithm Based on Quantum-behaved Particle Swarm and Adaptive Grid
摘要
Abstract
In order to find more true Pareto optimal solutions and improve their uniformity of distribution, a multi-objective quantum particle swarm optimization algorithm based on quantum-behaved particle swarm optimization (QPSO) and adaptive grid (MOQPSO) is proposed. MOQPSO makes full use of the superiority of quantum-behaved particle swarm optimization to approximate the true Pareto optimal solutions quickly, and Gaussian mutation operator is introduced to enhance the diversity of searched solutions. MOQPSO reserves the found Pareto optimal solutions by setting an external memory, and then updates and maintains the optimal solutions based on adaptive grid, in order to guide the particle swarm finding the true Pareto optimal solutions finally by the leader particles from external memory. Simulation results denote that MOQPSO is of better convergence and more uniform distributing performance.关键词
多目标优化/量子行为特性粒子群优化/高斯变异/自适应网格/Pareto最优解Key words
multi-objective optimization/ quantum-behaved panicle swarm optimization/ Gaussian mutation/ adaptive grid/ Pareto optimal solution分类
数理科学引用本文复制引用
施展,陈庆伟..基于量子行为特性粒子群和自适应网格的多目标优化算法[J].信息与控制,2011,40(2):214-220,226,8.基金项目
国家自然科学基金资助项目(60975075) （60975075）
教育部高校博士点基金资助项目(20070288022) （20070288022）
江苏省自然科学基金资助项目(BK2008404). （BK2008404）
