面向柔性作业车间调度问题的混沌编码量子粒子群优化算法  

Chaotic-encode Quantum PSO Algorithm for Flexible Job-shop Scheduling Problem

作　　者：胥远兴 张孟健 王德光 Xu Yuanxing;Zhang Mengjian;Wang Deguang(School of Electrical Engineering,Guizhou University,Guiyang 550025,China;School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China)

机构地区：[1]贵州大学电气工程学院,贵州贵阳550025 [2]华南理工大学计算机科学与工程学院,广东广州510006

出　　处：《系统仿真学报》2024年第10期2371-2382,共12页Journal of System Simulation

基　　金：贵州省省级科技计划(黔科合基础-ZK[2022]一般103);贵州大学科研基金(贵大特岗合字[2021]04号);贵州省教育厅创新群体(黔科合支撑[2021]012)。

摘　　要：为解决柔性作业车间调度问题,提出一种混沌编码量子粒子群优化算法。针对标准量子粒子群优化算法中粒子过早收敛于局部最优值的缺点,提出具有扰动行为的自适应收缩-扩张系数和关联粒子适应度值的计算方法,改善算法的全局搜索能力;通过引入混沌边界变异策略,减少粒子大量聚集在边界的概率,增加种群的多样性来提高搜索最优解的能力;针对量子粒子群优化算法的迭代特性,设计一种适用的混沌编码策略。将提出的改进量子粒子群优化算法应用于柔性作业车间调度问题,并通过多种基准算例与标准量子粒子群优化算法、粒子群优化算法和混合遗传算法进行对比,验证所提算法的性能。实验结果表明:混沌编码量子粒子群优化算法具有更好的稳定性和更强的寻优能力。To solve the flexible job-shop scheduling problem(FJSP),a chaotic-encode quantum PSO(CQPSO)algorithm is proposed.Aiming at the premature convergence of particles to local optimum in standard QPSO,the methods for computing the adaptive contraction-expansion coefficient and mean best position using fitness values of associated particles are proposed to improve the global search ability of QPSO.Through chaotic boundary variation strategy,the probability of a large number of particles gathering at the boundary is reduced and the population diversity is increased to enhance the ability of searching the optimal solution.According to the iterative property of QPSO,a chaotic-encode strategy is designed.The proposed CQPSO is applied to solve FJSP and the result is compared with QPSO,PSO,and hybrid genetic algorithm result on several benchmarks to confirm the performance.The experimental results show that CQPSO has the better stability and the stronger optimization ability.

关 键 词：量子粒子群优化算法 柔性作业车间调度 扰动行为 混沌映射 收缩-扩张系数 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置] TP391.9[自动化与计算机技术—控制科学与工程]