摘要
针对传统的粒子群优化算法在求解环境/经济调度中存在控制参数多和局部搜索精度低等问题,提出一种基于多目标量子粒子群优化算法的环境/经济调度问题的求解方法.该算法利用具有量子行为特性的粒子搜索解空间,引入改变作用区间的变异算子增强全局搜索能力,并采用基于粒子多样性的方法更新全局最优的领导粒子.仿真结果表明,该算法是有效的,所求Pareto解集能逼近真实的Pareto解集且具有良好的分布性.
Environmental/economic dispatch problems with traditional particle swarm optimization algorithm have disadvantages of many control parameters and low local search accuracy.A method based on a quantum-behaved multi-objective particle swarm optimization algorithm is proposed.The algorithm uses a quantum-behaved particle updating strategy to expand the search capability.A mutation operator with action range varying over time is introduced to escape the local Pareto front and an approach based on particle diversity is used to update the global particle leaders.The simulation results show that this algorithm is capable of generating excellent approximation of the true Pareto front.
作者
章恩泽
尹丹
ZHANG Enze;YIN Dan(School of Information Engineering,Yangzhou Universty,Yangzhou 225127,China)
出处
《扬州大学学报（自然科学版）》
CAS
北大核心
2019年第4期48-53,共6页
Journal of Yangzhou University：Natural Science Edition
基金
江苏省高等学校自然科学基金资助项目(18KJB120011)
关键词
环境/经济调度
多目标优化
量子粒子群算法
environmental/economic dispatch
multi-objective optimization
quantum-behaved particle swarm algorithm
