Grey wolf optimizer (GWO) is a recently proposed optimization algorithm inspired from hunting behavior of grey wolves in wild nature. The main challenge of GWO is that it is easy to fall into local optimum. Owing to the ergodicity of chaos, this paper incorporates the chaos theory into the GWO to strengthen the performance of the algorithm. Three different chaotic strategies with eleven various chaotic map functions are investigated and the most suitable one is regarded as the proposed chaotic GWO. Extensive experiments are made to compare the proposed chaotic GWO against other metaheuristics including adaptive differential evolution (JADE), cellular genetic algorithm, artificial bee colony, evolutionary strategy, biogeography-based optimization, comprehensive learning particle swarm optimization, and GWO. In addition, the proposal is also successfully applied to practical engineering problems. Experimental results demonstrate that the chaotic GWO is better than its compared metaheuristics on most of test problems and engineering optimization problems.

灰狼优化器（GWO）是最近提出的优化算法，其灵感来自野外灰狼的狩猎行为。GWO的主要挑战是容易陷入局部最优。由于混沌的遍历性，本文将混沌理论纳入了GWO中，以增强算法的性能。研究了三种具有11种不同的混沌图函数的不同混沌策略，最合适的一种被认为是所提出的混沌GWO。进行了广泛的实验，将拟议的混沌GWO与其他元启发式方法进行比较，包括自适应差分进化（JADE），细胞遗传算法，人工蜂群，进化策略，基于生物地理的优化，综合学习粒子群优化和GWO。此外，该建议书也成功地应用于实际工程问题。实验结果表明，在大多数测试问题和工程优化问题上，混沌GWO优于其比较的启发式算法。