Hybrid Whale Optimization Algorithm with Differential Evolution and Chaotic Map Operations

Original Whale Optimization Algorithm (WOA) is a meta-heuristic optimization one generated by imitating whale hunting. It has achieved good performance in many optimization problems. However, WOA is prone to lose the diversity of solutions in the iterative process and fall into local optima, which makes the optimization process stagnate. This work aims to solve this problem by combining the chaos theory and the idea of a Differential Evolution (DE) algorithm to enhance the randomness and diversity of solutions generated by WOA, so as not to fall into a locally optimal solution in the iteration as far as possible. Specifically, this work proposes a Chaotic Differential WOA (CDWOA), which is tested with 10 benchmark functions, and final optimization results are obtained to demonstrate its performance. In addition, experiments with higher dimensions are conducted to evaluate the performance of CDWOA. The running time of CDWOA for each problem is tested to demonstrate the efficiency of CDWOA. The comparison of experimental results shows that it outperforms its typical peers in solving both low-dimensional and high-dimensional problems of single-objective optimization.