改进哈里斯鹰优化算法求解作业车间调度问题

Improved Harris Hawks Optimization Algorithm for Job-Shop Scheduling Problem

针对哈里斯鹰优化(harris hawks optimization,HHO)算法在解决以最小化最大完成时间为目标的作业车间调度问题,存在收敛后期陷入局部最优和早熟的缺陷问题,提出一种改进哈里斯鹰优化算法。在哈里斯鹰算法中引入变邻域搜索,更好地深入探索最优哈里斯鹰个体的邻域,提高算法的局部开发能力;当算法的最优解停滞更新时,采用逐维柯西高斯变异策略产生新的候选解,增强种群的多样性和跳出局部最优的可能性;引入逐维自适应变异策略,扰动最优解,避免算法过于早熟。求解作业车间调度问题的仿真结果表明,改进哈里斯鹰优化(IHHO)算法实验结果优于哈里斯鹰优化(HHO)算法、灰狼优化(GWO)算法以及鲸鱼优化算法(WOA),有效地避免了哈里斯鹰优化算法早熟,陷入局部最优的问题。

Aiming at the defect that harris hawks optimization(HHO)algorithm falls into local optimization and premature in the later stage of convergence in solving the job shop scheduling problem with the goal of minimizing the maximum completion time,an improved harris hawks optimization algorithm is proposed.The variable neighborhood search is introduced into harris hawks algorithm to better explore the neighborhood of the optimal harris hawk individual and improve the local development ability of the algorithm;When the optimal solution of the algorithm stagnates and updates,a new candidate solution is generated by using the dimension by dimension cauchy gaussian mutation strategy to enhance the diversity of the population and the possibility of jumping out of the local optimization;The dimension by dimension adaptive mutation strategy is introduced to disturb the optimal solution to avoid premature algorithm.The simulation results of solving job shop scheduling problem show that the experimental results of improved harris hawks optimization(IHHO)algorithm are better than harris hawks optimization(HHO)algorithm,gray wolf optimization(GWO)algorithm and whale optimization algorithm(WOA),which effectively avoids the problem of premature harris hawks optimization algorithm and falling into local optimization.