A new whale optimisation algorithm based on self-adapting parameter adjustment and mix mutation strategy

ABSTRACT

The Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales, is a recently developed meta-heuristic algorithm. However, WOA exists the defect of easily falling into local optimum. This paper proposes a new WOA based on a self-adapting parameter adjustment and a mix mutation strategy (abbreviated as SMWOA). The self-adapting parameter adjustment strategy based on a normal distribution is adopted to make the algorithm jump out of local optima and enhance the global exploration capability. Meanwhile, in order to obtain a better tradeoff between the exploration and exploitation capabilities of the WOA, a novel mix mutation strategy is embedded in the proposed algorithm. The performance of SMWOA is tested on 23 benchmark functions and test suites composed of four engineering design problems. Experimental results and statistical analyses indicate that the proposed algorithm is very competitive when compared to the original WOA as well as some state-of-the-art algorithms.

KEYWORDS:

• Engineering design problems
• exploration and Exploitation
• function optimization problems
• whale optimization algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.