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.

中文翻译：

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基于自适应参数调整和混合变异策略的鲸鱼优化新算法

摘要 鲸鱼优化算法 (WOA) 模仿座头鲸的社会行为，是最近开发的元启发式算法。但是，WOA存在容易陷入局部最优的缺陷。本文提出了一种基于自适应参数调整和混合变异策略的新 WOA（简称 SMWOA）。采用基于正态分布的自适应参数调整策略，使算法跳出局部最优，增强全局探索能力。同时，为了在 WOA 的探索和开发能力之间获得更好的权衡，在所提出的算法中嵌入了一种新的混合变异策略。SMWOA 的性能在 23 个基准功能和测试套件上进行了测试，这些测试套件由四个工程设计问题组成。