Contents I. Introduction
Whale optimization algorithm (WOA) [1] is a new optimization algorithm proposed by Australian scholar Mirjalili in 2016 to simulate the predatory behaviour of humpback whale population in nature [2]. Compared with the common optimization algorithm, the algorithm is simpler to implement, requires fewer parameters, and has faster convergence speed while ensuring accuracy. It has been successfully applied in job shop scheduling [3], routing control [4], feature selection [5], battery parameter estimation [6] and so on. However, with the continuous improvement of engineering requirements, more and more scholars at home and abroad have made new improvements on whale algorithm. In reference [7], simulated annealing algorithm is embedded in WOA to locate the possible location of the optimal solution. Ling et al. applied Lévy flight path to WOA, and proved its superiority in dealing with the problem of limited and unknown search space [8]. Wu improves the operation of spiral update position, and solves the problem of diversity loss in the later stage of the algorithm [9]. Mohamed combines WOA with local search when dealing with shop floor scheduling, and the algorithm of Nawaz-Enscore-Ham is applied in the end [10]. Bilal uses Hill clipping algorithm at the end of each iteration, which greatly reduces the amount of convergence calculation [11]. Hu proposes an improved WOA and uses a new control parameter inertia weight to adjust the influence of the current solution [12].
