摘要
在广义粒子群优化模型基础上,结合工艺规划问题的特性,设计了求解工艺规划问题的改进广义粒子群优化算法.该算法采用当前粒子与个体极值库、种群极值库进行交叉操作的方式,使粒子能够从个体极值和种群中获取更新信息,引入变邻域搜索算法作为粒子的局部搜索策略.实例测试结果显示,与其他算法相比,本文算法在求解工艺规划问题时具有更高的求解效率和更好的稳定性.
An Improved General Particle Swarm Optimization( IGPSO) algorithm was proposed for process planning problem based on the GPSO model and the characteristics of process planning problem. Crossover operations were utilized to achieve the particles to obtain updated information from individual extreme library and population extreme library. Variable Neighborhood Search algorithm was introduced as a local search strategy for particles. A set of instances have been conducted to examine the proposed algorithm and the comparisons among other algorithms appeared in current literature were also presented. The experimental results showed the proposed algorithm had higher efficiency and better stability in solving process planning problems.
作者
文笑雨
罗国富
李浩
肖艳秋
乔东平
李晓科
WEN Xiaoyu;LU0Guofu;LI Hao;XIA0Yanqiu;QIA0Dongping;Li Xiaoke(Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment,Zhengzhou University of Light Industry,Zhengzhou 450002,China)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2018年第6期59-63,87,共6页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(51775517)
河南省高等学校重点科研项目(15A460037)
河南省产学研合作计划项目(172107000019)
郑州轻工业学院博士科研基金资助项目(2014BSJJ0023)
关键词
广义粒子群优化算法
工艺规划
变邻域搜索
组合优化
general particle swarm optimization
process planning
variable neighborhood search
combination optimization
