Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-02 Cooperative journals: 《计算机应用研究》
Abstract： To overcome whale optimization algorithm’s disadvantages of poor convergence and being easily trapped in local optima in solving job-shop scheduling problem, this paper proposed a quantum whale optimization algorithm (QWOA) based on the idea of quantum computation. Then it provided the computational complexity analysis, global convergence proof and simulation experiments of QWOA. Based on a set of 11 JSP benchmark instances, the simulation experiments show that QWOA has better results than whale optimization algorithm (WOA) , cuckoo search (CS) and grey wolf optimizer (GWO) , in the minimum value, average value and success rate. Finally, this paper concludes that QWOA has the merits of higher convergence accuracy, higher local optima avoidance and better exploration.
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