Grey wolf optimizer (GWO) is a nature-inspired stochastic meta-heuristic of the swarm intelligence field that mimics the hunting behavior of grey wolves. Differential evolution (DE) is a popular stochastic algorithm of the evolutionary computation field that is well suited for global optimization. In this part, we introduce a new algorithm based on the hybridization of GWO and two DE variants, namely the GWO-DE algorithm. We evaluate the new algorithm by applying various numerical benchmark functions. The numerical results of the comparative study are quite satisfactory in terms of performance and solution quality.

翻译：灰狼优化器（GWO）是一种受自然启发的随机元启发式算法，属于群体智能领域，其模拟了灰狼的狩猎行为。差分进化（DE）是进化计算领域一种流行的随机算法，非常适用于全局优化。本文提出了一种基于GWO与两种DE变体混合的新算法，即GWO-DE算法。我们通过应用多种数值基准函数对该新算法进行了评估。对比研究的数值结果在性能和解质量方面均相当令人满意。