A proposed whale search algorithm with adaptive random walk

Abstract

In this paper, a variant of the recently introduced whale optimization algorithm (WOA) was proposed based on adaptive switching of random walk per individual search agent. WOA is recently proposed bio-inspired optimizers that employ two different random walks. The original optimizer stochastically switches between the two random walk at each iteration regardless of the search agents performance and regardless of the fitness terrain around it. In the proposed adaptive walk whale optimization algorithm (AWOA), an adaptive switching between the two random walk is recommended based on the agent's performance. Moreover, a random explorative switch of the walk is applied to allow search agents to try different walks. The proposed AWOA was benchmarked using 29 standard test functions with uni-modal, multi-modal, and composite test functions. Performance over such functions proves the capability of the proposed variant to outperform the original WOA.