This paper proposes new versions of Harris Hawks Optimizer (HHO) incorporated the survival-of-the-fittest principle of evolutionary algorithms. HHO is the recent swarm-based optimization algorithm imitating the surprise pounce behaviour of Harris’ hawks chasing style. HHO can show different patterns of the exploration and exploitation. It has a simple and time-varying structure, which further assist a smooth transition between the core phases. It has two main phases to iterate toward the optimal solution: exploration and exploitation. In the exploration phase, the current solution is either randomly modified based on any solution selected randomly or rebuilt from scratch. In evolutionary algorithms, selecting any solution from swarm basically relies on the natural selection principle of the survival-of-the-fittest to accelerate convergence. To make use of such principle, three selection strategies (i.e., tournament, proportional and linear rank-based methods) are employed in the exploration phase of HHO and introduces three new versions, which are Tournament HHO (THHO), Proportional HHO (PHHO), and Linear-Rank HHO (LHHO). In order to evaluate the performance of the proposed HHO versions, 23 well-regarded benchmark functions with various sizes and complexities are utilized as well as three real-world engineering problems. The sensitivity of proposed HHO versions to their parameter settings are studied and analyzed. Thereafter, a scalability study is conducted to show the effect of the population dimensions on the proposed HHO versions. Comparative evaluation shows that THHO version has superiority over other proposed HHO versions. Furthermore, the proposed HHO versions show enhanced trade off between the exploratory and exploitative trends and a better local optima avoidance. They are able to produce viable results competitively comparable with other eleven state-of-the-art methods using the same benchmark functions. Interestingly, the proposed variants of HHO are able to yield new results for some benchmark functions. Furthermore, three real-world engineering optimization problem of IEEE CEC2011 are also used in the evaluation process. Again, the proposed variants of HHO are able to achieve the best results.

本文提出了新版本的Harris Hawks Optimizer（HHO），它结合了进化算法的优胜劣汰原理。HHO是最近基于群体的优化算法，模仿了哈里斯鹰追逐风格的突袭行为。HHO可以显示不同的勘探和开发模式。它具有简单且随时间变化的结构，进一步有助于核心阶段之间的平稳过渡。它有两个主要阶段可以迭代到最佳解决方案：探索和开发。在探索阶段，可以根据随机选择的任何解决方案对当前解决方案进行随机修改，或者从头开始重建。在进化算法中，从群体中选择任何解决方案基本上都依赖于自然选择原理。适者生存加快融合。为了利用这种原理，在HHO的探索阶段采用了三种选择策略（即基于比赛，比例和基于线性秩的方法），并引入了三种新版本，即Tournament HHO（THHO），Proportional HHO（PHHO） ，以及线性排名HHO（LHHO）。为了评估建议的HHO版本的性能，利用了23个具有各种大小和复杂性的，广受好评的基准功能，以及三个实际的工程问题。研究和分析了建议的HHO版本对其参数设置的敏感性。此后，进行了可伸缩性研究，以显示人口规模对拟议的HHO版本的影响。比较评估表明，THHO版本优于其他提议的HHO版本。此外，建议的HHO版本显示出探索性和开发性趋势之间的权衡得到了增强，并且更好地避免了局部最优。使用相同的基准功能，它们能够与其他十一种最新方法竞争，从而产生可行的结果。有趣的是，HHO的建议变体能够为某些基准功能产生新的结果。此外，在评估过程中还使用了IEEE CEC2011的三个实际工程优化问题。同样，建议的HHO变体能够达到最佳结果。建议的HHO变体能够为某些基准功能产生新结果。此外，在评估过程中还使用了IEEE CEC2011的三个实际工程优化问题。同样，建议的HHO变体能够达到最佳结果。建议的HHO变体能够为某些基准功能产生新结果。此外，在评估过程中还使用了IEEE CEC2011的三个实际工程优化问题。同样，建议的HHO变体能够达到最佳结果。