ABSTRACT The article presents a Hybrid Multi-Evolutionary Algorithm designed to solve optimization problems. The Genetic Algorithm and Evolutionary Strategy work together to improve the efficiency of optimization and increase resistance to getting stuck to sub-optimal solutions. Genetic Algorithm and Evolutionary Strategy can periodically exchange the best individuals from each other. The algorithm combines the ability of the Genetic Algorithm to explore the search space and the ability of the Evolutionary Strategy to exploit the search space. It maintains the right balance between the exploration and exploitation of the search space. The results of the experiments suggest that the proposed algorithm is more effective than the Genetic Algorithms and Evolutionary Strategy used separately, and can be an effective tool in solving complex optimization problems.

中文翻译：

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求解优化问题的混合多进化算法

摘要 本文提出了一种旨在解决优化问题的混合多进化算法。遗传算法和进化策略协同工作，以提高优化效率并增加对陷入次优解决方案的抵抗力。遗传算法和进化策略可以周期性地相互交换最好的个体。该算法结合了遗传算法探索搜索空间的能力和进化策略探索搜索空间的能力。它在搜索空间的探索和利用之间保持适当的平衡。实验结果表明，所提出的算法比单独使用的遗传算法和进化策略更有效，