Whale optimization algorithm (WOA) is a relatively new population-based metaheuristic optimization method, which has the advantage of fewer control parameters, strong global optimization ability and easy to implement. However, when being used for high-dimensional problems, WOA may be trapped in the local optimum. In this study, we propose an effective whale optimization algorithm called EWOA. Inspired by particle swarm optimization (PSO), a modified position-updated equation by introducing dynamic inertia weight parameter to guide the search of new candidate individuals is presented. In addition, in order to make full use of and balance the exploration and the exploitation of WOA, a nonlinea... More >

Grey wolf optimizer (GWO) is an effective meta-heuristic technique which has been widely utilized to solve numerical optimization as well as real-world applications. However, GWO has some shortcomings, i.e., low solution accuracy, slow convergence, and easy stagnation at local optima in solving complex problems. To tackle these shortcomings, an enhanced GWO called EGWO is developed in this study. This enhancement is achieved by embedding three novel strategies into the basic GWO to improve its performance. Firstly, a new transition mechanism is designed instead of the original strategy to obtain a good transition from the exploration to exploitation. Secondly, the cuckoo search algorithm is... More >