A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems

Abstract

Whale Optimization Algorithm (WOA), as a newly developed meta-heuristic algorithm, performs well in solving optimization problems. A WOA with chaos mechanism based on quasi-opposition (OBCWOA) is proposed in this paper to overcome the slow convergence speed of the original WOA and to avoid being trapped in local optimal solutions when dealing with high-dimensional problems. We applied two strategies to the original WOA: using chaos mechanism to generate initial value to improve convergence speed of the algorithm and using the opposition-based learning method to balance exploration and development ability of the algorithm to help the algorithm jump out of local optimal solutions. The proposed algorithm is compared with other algorithms on unimodal functions, multimodal functions and fixed dimensional multimodal functions, and is applied to a famous engineering design problem. Results show that combination of the two strategies can improve convergence speed and enhance global search ability of the original WOA. OBCWOA proposed in this paper performs better than the other existing algorithms.