基于广义粒子群优化模型的工艺规划方法研究
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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.
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.
引文
[1] LI X Y,GAO L,WEN X Y. Application of an efficient modified particle swarm optimization algorithm for process planning[J]. International journal of advanced manufacturing technology,2013,67(5/8):1355-1369.
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[4]黄风立,顾金梅,张礼兵,等.基于禁忌制造特征动态调整的STEP-NC工艺路线蚁群优化方法[J].中国机械工程,2016,27(5):596-602.
[5]范孝良,吴学华,赵爱林,等.一种基于蝙蝠算法的工艺规划方法[J].图学学报,2015,36(6):856-861.
[6] YUSOF Y, LATIF K. Survey on computer-aided process planning[J]. International journal of advanced manufacturing technology,2014,75(1/4):77-89.
[7]高海兵,周驰,高亮.广义粒子群优化模型[J].计算机学报,2005,28(12):1980-1987.
[8]彭传勇,高亮,邵新宇,等.求解作业车间调度问题的广义粒子群优化算法[J].计算机集成制造系统,2006,12(6):911-917.
[9]潘全科,王文宏,朱剑英,等.基于粒子群优化和变邻域搜索的混合调度算法[J].计算机集成制造系统,2007,13(2):323-328.
[10] LI W D,ONG S K,NEE A. Optimization of process plans using a constraint-based tabu search approach[J]. International journal of production research,2004,42(10):1955-1985.
[11]何铁芳.基于改进广义粒子群优化的开放式车间调度方法研究[D].武汉:华中科技大学机械科学与工程学院,2009.
[12]程适,陈俊风,孙奕菲,等.数据驱动的发展式头脑风暴优化算法综述[J].郑州大学学报(工学版),2018,39(3):22-28.
[2] LIAN K L,ZHANG C Y,SHAO X Y,et al. Optimization of process planning with various flexibilities using an imperialist competitive algorithm[J]. International journal of advanced manufacturing technology,2012,59(5/8):815-828.
[3]武轩.复杂零件工艺路线重用与优化技术及在数控机床中的应用[D].杭州:浙江大学机械工程学院,2014.
[4]黄风立,顾金梅,张礼兵,等.基于禁忌制造特征动态调整的STEP-NC工艺路线蚁群优化方法[J].中国机械工程,2016,27(5):596-602.
[5]范孝良,吴学华,赵爱林,等.一种基于蝙蝠算法的工艺规划方法[J].图学学报,2015,36(6):856-861.
[6] YUSOF Y, LATIF K. Survey on computer-aided process planning[J]. International journal of advanced manufacturing technology,2014,75(1/4):77-89.
[7]高海兵,周驰,高亮.广义粒子群优化模型[J].计算机学报,2005,28(12):1980-1987.
[8]彭传勇,高亮,邵新宇,等.求解作业车间调度问题的广义粒子群优化算法[J].计算机集成制造系统,2006,12(6):911-917.
[9]潘全科,王文宏,朱剑英,等.基于粒子群优化和变邻域搜索的混合调度算法[J].计算机集成制造系统,2007,13(2):323-328.
[10] LI W D,ONG S K,NEE A. Optimization of process plans using a constraint-based tabu search approach[J]. International journal of production research,2004,42(10):1955-1985.
[11]何铁芳.基于改进广义粒子群优化的开放式车间调度方法研究[D].武汉:华中科技大学机械科学与工程学院,2009.
[12]程适,陈俊风,孙奕菲,等.数据驱动的发展式头脑风暴优化算法综述[J].郑州大学学报(工学版),2018,39(3):22-28.