摘要
基本遗传算法在求解大规模多目标优化问题时会出现早熟和搜索效率低等问题。针对这些问题,对基本遗传算法引入了邻域操作、自适应策略和混沌优化等多种改进策略,研究设计了一种有机结合各种改进策略的改进遗传算法流程。应用实例的仿真试验表明改进算法可行,且在求解大规模多目标优化问题时较基本遗传算法具有精度和速度优势。
Basic genetic algorithm has been confronted with several problems such as premature and low- search-speed when it is applied to solve military multi-object optimizing problems. To avoid these problems, some methods including adjacent-domain operations, adaptability and chaos have been taken into consideration in this paper to improve the capability of the algorithm. Furthermore, the paper designs an improved algorithmic flow structure which organically combined all the above methods. The simulation results show that the improved algorithm is more reliable and more accurate than the basic algorithm in solving multi-object optimizing problems.
出处
《系统管理学报》
北大核心
2007年第3期315-319,共5页
Journal of Systems & Management
基金
国家863计划资助项目(2004AA115120)
关键词
多目标优化
遗传算法
邻域操作
自适应策略
混沌
multi-object Optimization
genetic algorithm
adjacent-domain operation
adaptability
chaos
作者简介
张雄飞（1971-），男，工程师，博士生。研究方向为智能化作战模拟、决策与决策支持系统。
