Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

中文翻译：

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基于随机跳变更新和随机控制参数的改进鲸鱼优化算法

鲸鱼优化算法（WOA）是在元启发式算法领域中相对较新的算法。与其他完善的优化算法相比，WOA可以表现出高效的性能，但是在复杂的多峰函数中仍然存在过早收敛和易于陷入局部最优的问题，因此本文提出了一种改进的WOA，并提出了随机跳变更新策略和随机控制参数策略，提高了WOA的勘探开发能力。本文使用24个著名的基准函数对算法进行测试，包括10个单峰函数和14个多峰函数。实验结果表明，该算法在21个函数上的收敛精度优于原始算法。