摘要
针对实际工况下置换流水车间调度问题,文中以最小化完工时间为目标对标准布谷鸟算法进行了改进。为提高优化解的稳定性和算法的计算精度,该算法将淘汰概率引入动态自适应机制,将局部搜索引入差分进化机制,并在初始种群的生成中引入NEH算法。文中将改进的布谷鸟算法运用于解决实际工况下的置换流水车间调度问题,通过与标准布谷鸟算法仿真优化结果进行对比,证明了改进布谷鸟算法具有更好的解的稳定性和更高的寻优精度。
Aiming at the problem of displacement flow shop scheduling under actual working conditions, the standard cuckoo algorithm was improved with the goal of minimizing the completion time. In order to improve the stability of the optimized solution and the computational accuracy of the algorithm, the proposed algorithm introduced the elimination probability into the dynamic adaptive mechanism. Besides, the proposed algorithm also introduced the local search into the differential evolution mechanism, and applied the NEH algorithm in the initial population generation. The improved cuckoo search was applied to solve the permutation flow shop scheduling problem under actual working conditions. The results showed that compared with the simulation optimization results of the standard cuckoo search, the improved cuckoo search had better stability and higher precision.
作者
邴孝锋
陶翼飞
董圆圆
孙思汉
BING Xiaofeng;TAO Yifei;DONG Yuanyuan;SUN Sihan(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650000,China)
出处
《电子科技》
2019年第10期60-64,共5页
Electronic Science and Technology
基金
国家自然科学基金地区基金(51566006)~~
