摘要
针对传统的单一加工车间灵活度不高的问题,提出了一种多目标改进白鲸优化(IBWO)算法的车间调度方法。首先,建立了以最小化最大完工时间和最小化能耗为目标的多目标非置换流水车间调度问题模型,IBWO根据调度问题的特点,设计了双层实数编码机制表示问题的解;然后,利用非支配关系和拥挤度排序算法评价了多目标解之间的优劣关系,使用实数交叉和变邻域搜索策略,对建立的模型进行了求解;最后,将IBWO分别与白鲸优化算法(BWO)、使用遗传交叉策略但未使用变邻域搜索策略算法(BWO-1)、使用变邻域搜索策略但未使用交叉策略算法(BWO-2)进行了对比,并进一步将其与非支配排序遗传算法2(NSGA2)、NSGA3和强度帕累托进化算法2(SPEA2)多目标优化算法进行了对比。研究结果表明:使用测试算例仿真对比后,可使用收敛性指标迭代距离(GD)、多样性指标、综合性指标反世代距离(IGD)对结果进行评判,改进白鲸优化算法指标可至少在60%的测试算例上取得优势地位;使用实数交叉策略和变邻域搜索策略的改进白鲸优化算法,可弥补原始白鲸算法局部搜索能力较差的缺点,有效增强算法的有效性和稳定性,提高改进算法的搜索能力,为实际生产提供一定的指导。
Aiming at the problem of the low flexibility of the traditional single machining shop,a multi-objective improved beluga whale optimization(IBWO)algorithm was proposed to solve the problem.Firstly,a multi-objective non-permutation flow shop scheduling model was established to minimize the maximum completion time and minimize the energy consumption,according to the characteristics of the scheduling problem,a two-layer real number coding mechanism was designed to represent the solution of the problem.Secondly,the relationship between the advantages and disadvantages of the multi-objective solution was evaluated by using the non-dominant relation and crowding ranking algorithm,and the real number crossing and variable neighborhood search strategy were used to solve the problem.Finally,the IBWO algorithm was respectively compared with the beluga whale optimization(BWO)algorithm,the algorithm that using real number crossing but not using variable neighborhood search strategy(BWO-1),the algorithm that using variable neighborhood search strategy but not using real number crossing(BWO-2).Furthermore,the IBWO was compared with multi-objective optimization algorithms,such as non-dominated sorting genetic algorithm-II(NSGA2),NSGA3 and strength Pareto evolutionary algorithm 2(SPEA2).The research results show that,the index of IBWO algorithm achieves a dominant position in at least 60%test cases by using the generational distance(GD),diversity index and inverted generational distance(IGD)to evaluate the comparative results.The IBWO algorithm using real number crossing and the variable neighborhood search strategy to make up for the shortcomings of the BWO algorithm s poor local search ability,effectively enhance the effectiveness and stability of the algorithm,improve the search ability of the improved algorithm.It can provide some guidances for the practical production.
作者
丁祎
宋欣钢
皇涛
DING Yi;SONG Xingang;HUANG Tao(School of Mechanical and Electrical Engineering,Huanghe Jiaotong University,Jiaozuo 454950,China;School of Materials Science and Engineering,Henan University of Science and Technology,Luoyang 471023,China)
出处
《机电工程》
CAS
北大核心
2024年第12期2232-2242,共11页
Journal of Mechanical & Electrical Engineering
基金
河南省重点研发专项(231111231700)
中原英才计划项目(中原青年拔尖人才)([2023]11)
河南省高等学校重点科研项目(24A430015)
河南省智能制造技术与装备工程技术研究中心项目(3118210370)。
关键词
车间调度模型
多目标优化
改进白鲸优化算法
实数交叉
变邻域搜索
迭代距离
反世代距离
白鲸优化算法
shop scheduling model
multi-objective optimization
improved beluga whale optimization algorithm(IBWO)
real number crossing
variable neighborhood search
generational distance(GD)
inverted generational distance(IGD)
beluga whale optimization(BWO)
