Optimization algorithms have made considerable advancements in solving complex problems with the ability to be applied to innumerable real-world problems. Nevertheless, they are passed through several challenges comprising of equilibrium between exploration and exploitation capabilities, and departure from local optimums. Portioning the population into several sub-populations is a robust technique to enhance the dispersion of the solution in the problem space. Consequently, the exploration would be increased, and the local optimums can be avoided. Furthermore, improving the exploration and exploitation capabilities is a way of increasing the authority of optimization algorithms that various researches have been considered, and numerous methods have been proposed. In this paper, a novel hybrid multi-population algorithm called HMPA is presented. First, a new portioning method is introduced to divide the population into several sub-populations. The sub-populations dynamically exchange solutions aiming at balancing the exploration and exploitation capabilities. Afterthought, artificial ecosystem-based optimization (AEO) and Harris Hawks optimization (HHO) algorithms are hybridized. Subsequently, levy-flight strategy, local search mechanism, quasi-oppositional learning, and chaos theory are utilized in a splendid way to maximize the efficiency of the HMPA. Next, HMPA is evaluated on fifty unimodal, multimodal, fix-dimension, shifted rotated, hybrid, and composite test functions. In addition, the results of HMPA is compared with similar state-of-the-art algorithms using five well-known statistical metrics, box plot, convergence rate, execution time, and Wilcoxon’s signed-rank test. Finally, the performance of the HMPA is investigated on seven constrained/unconstrained real-life engineering problems. The results demonstrate that the HMPA is outperformed the other competitor algorithms significantly.

中文翻译：

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HMPA：一种创新的混合多种群算法，基于人工生态系统和 Harris Hawks 工程问题优化算法

优化算法在解决复杂问题方面取得了相当大的进步，能够应用于无数现实世界的问题。尽管如此，他们还是经历了一些挑战，包括探索和开发能力之间的平衡，以及偏离局部最优。将总体划分为几个子总体是一种增强解决方案在问题空间中的分散度的强大技术。因此，将增加探索，并且可以避免局部最优。此外，提高探索和开发能力是提高优化算法权威性的一种方式，各种研究已经考虑，并提出了许多方法。在本文中，提出了一种称为 HMPA 的新型混合多种群算法。首先，引入了一种新的分割方法，将种群分成几个子种群。子种群动态交换解决方案，旨在平衡探索和开发能力。事后考虑，基于人工生态系统的优化 (AEO) 和 Harris Hawks 优化 (HHO) 算法是混合的。随后，巧妙地利用了逃逸策略、局部搜索机制、准对立学习和混沌理论，最大限度地提高了 HMPA 的效率。接下来，在 50 个单峰、多峰、固定维度、移位旋转、混合和复合测试函数上评估 HMPA。此外，将 HMPA 的结果与使用五个众所周知的统计指标、箱线图、收敛速度、执行时间和 Wilcoxon 符号秩检验的同类最新算法进行了比较。最后，HMPA 的性能在七个受约束/不受约束的现实工程问题上进行了研究。结果表明，HMPA 的性能明显优于其他竞争对手的算法。