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Multi-strategy boosted mutative whale-inspired optimization approaches

机译：多策略提升扭曲鲸鲸启发优化方法

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This paper presents an improved Whale Optimization Algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a 'shrinking' strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well. (C) 2019 Elsevier Inc. All rights reserved.
机译：本文介绍了一种改进的全局优化鲸鲸优化算法（WOA）。 WOA是最近推出的荟萃启发式算法，模仿驼背鲸的狩猎行为。由于其简单性在探索性和剥削性运营商以及令人满意的疗效中，该算法在许多工程和科学领域使用的基于良好的基于​​人群的方法中发现了它的位置。然而，在处理一些优化案例时，这种方法易于陷入本地最佳状态。为了进一步提高其探索性和剥削性能，将三种策略纳入原始方法，以保持利用和勘探趋势之间更好的平衡。首先，将混沌初始化阶段引入优化器中以启动混沌触发鲸鱼的群。然后，使用高斯突变来加强不断发展的人群的多样性水平。最后，使用“缩减”策略的混沌本地搜索来增强基本优化器的剥削倾向。为了验证改进的WOA的有效性，它将其与代表基准函数的四个元启发式和最先进的进化算法进行比较。试验结果和模拟显示，不仅提出的改进的WOA明显优于包括原始WOA的基本算法，而且还优于比较的最先进的方法。此外，建议的算法成功地应用于实现三个受限的工程测试用例，结果表明改进的WOA可以有效地处理受约束的功能。 （c）2019 Elsevier Inc.保留所有权利。

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