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Multi-strategy boosted mutative whale-inspired optimization approaches

机译：多策略助推变异鲸鱼启发的优化方法

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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：本文提出了一种用于全局优化的改进的鲸鱼优化算法（WOA）。 WOA是最近引入的模仿启发式座头鲸的狩猎行为的元启发式算法。由于其探索性和开发性操作员的简单性以及令人满意的效果，该算法已在许多工程和科学领域中广泛使用的基于人口的方法中找到了自己的位置。但是，这种方法在处理某些优化情况时很容易陷入局部最优。为了进一步提高其勘探和开发性能，将三种策略结合到原始方法中，以在开发和勘探趋势之间保持更好的平衡。首先，将混沌初始化阶段引入到优化器中，以启动成群的混沌触发的鲸鱼。然后，采用高斯突变来增强进化种群的多样性水平。最后，采用“缩小”策略的混沌局部搜索被用于增强基本优化器的开发倾向。为了验证改进的WOA的有效性，将其与代表性基准函数上的四种元启发式和最新的进化算法进行了比较。试验结果和模拟表明，不仅提出的改进的WOA明显优于包括原始WOA的那些基本算法，而且优于已比较的最新方法。此外，该算法成功地应用于实现三个约束工程测试用例，结果表明改进的WOA也可以有效地处理约束功能。 （C）2019 Elsevier Inc.保留所有权利。

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