This paper proposes a modified version of a contemporary metaheuristic named Harris Hawks Optimizer (HHO) that mimics the foraging strategies used by Harris hawks. It is first argued that exploration ability of HHO is weaker than its exploitation. In addition, the initial position of hawks has the greatest impact on the convergence of the solutions in a similar manner to other metaheuristic algorithms. Then, we applied the Fractional-Order Gauss and 2xmod1 Chaotic Maps to generate the initial population as well as applying the operators of the Moth-Flame Optimization (MFO) to improve the exploration of HHO. In addition, the concept of evolutionary Population Dynamics (EPD) is applied to prevent premature convergence and stagnation in local optima. The method proposed in this work is called FCHMD and evaluated using a set of thirty-six mathematical functions and five engineering problems. The results of the FCHMD are compared with a number of well-known metaheuristics. It can be observed that the FCHMD algorithm outperforms its competitors on the majority of case studies.

本文提出了一种改进的现代超启发式方法，称为Harris Hawks Optimizer（HHO），它模仿了Harris鹰使用的觅食策略。首先认为，HHO的勘探能力弱于其开采。此外，鹰的初始位置对解决方案的收敛性影响最大，其方式与其他元启发式算法相似。然后，我们应用了分数阶高斯和2xmod1混沌映射来生成初始种群，并应用了蛾-火焰优化（MFO）算子来改进对HHO的探索。另外，采用进化种群动力学（EPD）的概念来防止局部最优中的过早收敛和停滞。在这项工作中提出的方法称为FCHMD，并使用一组36个数学函数和5个工程问题进行评估。将FCHMD的结果与许多众所周知的元启发法进行了比较。可以看出，在大多数案例研究中，FCHMD算法的性能均优于竞争对手。