An improved whale optimization algorithm with cultural mechanism for high-dimensional global optimization problems

Abstract

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm which imitates the behavior of humpback whales. It has been widely accepted to solve problems in various engineering fields because its less required parameters and excellent optimal performance. Similarly to other meta-heuristic algorithm, WOA still has the disadvantage of trap in local optima. In this paper a cultural whale optimization algorithm(C-WOA) is proposed to prevent the algorithm from falling into local optimum, which combines cultural algorithm and whale optimization algorithm. The double evolution mechanism is considered in C-WOA for integrating the advantages of different mechanisms of population space and knowledge space, which effectively improves the performance of whale optimization algorithm. The proposed C-WOA is benchmarked on six well-known test functions, and the results show that the algorithm is effective in solving complex high-dimensional problems.