An improved Harris hawks optimizer for job-shop scheduling problem

In this paper, we propose an improved Harris hawk optimizer (IHHO) to overcome the shortcomings of Harris hawk optimizer (HHO) that there is aimless search in the global search process. IHHO is different from HHO in that during the global search process, the position of the Harris hawks is not random but moves according to the position of the best several Harris hawks in the population. The advantage of the new global search process in the IHHO is that it effectively improves the quality of candidate solutions. We use the proposed algorithm to solve job-shop scheduling problem, which is an NP-hard problem and also a very challenging optimization problem, because it is difficult to find a reasonable candidate solution in polynomial time. Many researchers have tried various methods to solve it, but failed to get an effective result. Therefore, we use it to verify the performance of the proposed algorithm. The experimental results of IHHO are compared with those of improved genetic algorithm, differential evolution and HHO. It can be seen from the experimental results that IHHO has obvious advantages compared with the results of other comparison algorithms in terms of convergence accuracy, convergence speed, stability and running time. The experimental results of Wilcoxon's rank sum test also show that the IHHO is essentially different from other comparison algorithms, which shows that the optimization performance of the IHHO is meaningful.