摘要
针对时间变量取值于正有理数集+、自变量的维数随时间可发生变化的一类动态多目标优化问题提出了一种求解的粒子群算法。该算法通过引入新的变异算子和自适应动态变化惯性因子,有效地避免了粒子群算法易陷入局部最优的缺陷;同时,给出了一种判断环境变化的有效规则,极大地增强了算法跟踪问题环境变化的能力,提高了算法的有效性。计算机仿真表明新算法对动态多目标优化问题的求解十分有效。
For a class of dynamic multi-objective optimization problems,in which the time variable was defined on positive rational number set and the dimension of independent variable changed with the time,a new particle swarm algorithm was proposed.By introducing the new mutation operator and the adaptive dynamic changing inertia weight,the proposed algorithm could effectively avoid the defects of plunging into the local optimal of the particle swarm algorithm.Also,an effective rule used to judge the environment changing was given to increase the environment changing detecting ability and improve the effectiveness of the algorithm.The simulation results show that the proposed algorithm is effective for solving the dynamic multi-objective optimization problems.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2011年第2期288-293,共6页
Journal of System Simulation
基金
国家自然科学基金(60805016)
陕西省自然科学基础研究计划项目(2009JM1013)
陕西省教育厅科研计划项目(09JK329)
宝鸡文理学院重点科研项目(ZK1013)
关键词
动态多目标优化
粒子群算法
变维空间
PARETO最优解
dynamic multi-objective optimization
particle swarm algorithm
changing dimension space
Pareto optimization solutions
