Differential Whale Optimization Algorithm

Abstract

The whale optimization algorithm (WOA) is a natural-inspired effective optimization algorithm by imitating the behavior of whales rounding up their prey. Due to the high capacity of WOA in terms of exploitation, it is likely to fall into a local optimum as individuals of WOA lack communication. In this paper, we innovatively enlarge the search range of WOA by performing a differential search for each individual in the population, thus enabling the proposed differential whale optimization algorithm (DWOA) to possess an ability of jumping out of the local optimum and meanwhile accelerating its convergence speed. Experimental results based on IEEE CEC2017 benchmark functions demonstrate the superiority of DWOA in terms of solution quality, population diversity, and convergence speed.