Bio-inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature-inspired, population-based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. Numerical experiments using 20 benchmark functions are carried out. The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well-known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching-Learning Based Optimization. Nine chaotic maps reported in the literature are tested. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way.

中文翻译：

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用于约束优化问题的混沌灰狼优化器

仿生算法因其能够在可接受的计算时间内为复杂的优化问题找到好的解决方案而变得流行。Gray Wolf Optimizer 是一种受自然启发、基于种群的元启发式算法，它模拟在灰狼群中观察到的社会等级和狩猎策略。尽管灰狼优化器已成功应用于解决不同的优化问题，但它可能会早熟收敛并陷入局部最优。为了克服这些缺点，本文提出了灰狼优化器的混沌版本，它不同于原始算法和之前发布的修改版本，因为它没有在控制算法执行的参数中添加混沌变量。反而，所提出的模型使用混沌变量来定义狼群中的狼群，这些狼群将用于在算法的每次迭代中指导狩猎过程。进行了使用 20 个基准函数的数值实验。将所提出模型的性能与原始灰狼优化器和其他著名算法（即粒子群优化、遗传算法、共生生物搜索和基于教学的优化）的性能进行比较。测试了文献中报道的九个混沌映射。结果表明，所提出的算法具有非常有竞争力的性能，切比雪夫映射在模拟的混沌映射中表现出最好的性能。所提出的算法可以直接集成到灰狼优化器的其他修改版本中。