This paper presents a self-adaptive multi-population approach based on genetic algorithm (GA) for solving dynamic resource allocation in shared hosting platforms. The proposed method, self-adaptive multi-population genetic algorithm (SAMPGA), is a multi-population GA strategy aimed at locating and tracking optima. This approach is based on preventing populations from searching in the same areas. Two adaptations to the basic approach are then proposed to further improve its performance. The first adapted algorithm, memory-based SAMPGA, is based on using explicit memory to store promising solutions and retrieve them upon detecting change in the environment. The second adapted algorithm, immigrants-based SAMPGA, is aimed at improving the technique used by SAMPGA to maintain a sustainable level of diversity needed for quick adaptation to the environmental changes. An extensive set of experiments is conducted on a variety of dynamic resource allocation scenarios, to evaluate the performance of the proposed approach. Results are also compared with those of self-organizing random immigrants GA using three well-known performance metrics. The experimental results indicate the effectiveness of the proposed approach.

中文翻译：

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用于共享托管平台动态资源分配的自适应多种群遗传算法

本文提出了一种基于遗传算法（GA）的自适应多种群方法，用于解决共享托管平台中的动态资源分配问题。所提出的方法，自适应多种群遗传算法（SAMPGA），是一种旨在定位和跟踪最优值的多种群遗传算法。这种方法是基于防止人群在同一地区进行搜索。然后提出了对基本方法的两种修改，以进一步提高其性能。第一个适应的算法，基于内存的 SAMPGA，基于使用显式内存来存储有希望的解决方案，并在检测到环境变化时检索它们。第二种适配算法，基于移民的SAMPGA，旨在改进 SAMPGA 使用的技术，以保持快速适应环境变化所需的可持续多样性水平。对各种动态资源分配场景进行了大量实验，以评估所提出方法的性能。还使用三个众所周知的性能指标将结果与自组织随机移民 GA 的结果进行了比较。实验结果表明了所提出方法的有效性。