关键词: 蝗虫优化算法, 量子旋转门, 作业车间调度问题, 收敛性证明, 混合算法

Abstract: As a new intelligent algorithm, Grasshopper Optimization Algorithm（GOA） solving Job-shop scheduling problem（JSP） is in line with the trend of intelligent manufacturing. However, the insufficient global optimization ability of GOA brings disadvantages of being easily trapped in local optima and poor convergence in solving JSP. To overcome the disadvantages above, this paper improves GOA based on the idea of quantum computation, and a Hybrid Grasshopper Optimization Algorithm（HGOA） is proposed by the quantum rotate gate. In addition, this paper provides the computational complexity analysis, global convergence proof, and simulations of HGOA based on a set of 11 JSP benchmark instances. Comparing HGOA with GOA, Whale Optimization Algorithm（WOA）, Cuckoo Search（CS） and Grey Wolf Optimizer（GWO） in simulations, the results show that HGOA has better performance in terms of mean value, minimum value, optimal success rate and number of iterations. Finally, this paper concludes that HGOA has the merits of better exploration, higher convergence accuracy and higher local optima avoidance.

Key words: grasshopper optimization algorithm, quantum rotate gate, job-shop scheduling problem, convergence proof, hybrid algorithm