ABSTRACT Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms.

中文翻译：

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GSA-LA：基于学习自动机的引力搜索算法

摘要无论引力搜索算法（GSA）的性能如何，它在高维问题中几乎无法避免局部最优。为了提高GSA的精度，需要对其参数进行微调。本研究介绍了一种基于学习自动机 (GSA-LA) 的引力搜索算法，用于优化连续问题。引力常数 G(t) 是一个重要的参数，用于调整搜索的准确性。在这项工作中，学习能力被用来根据自发反应选择 G(t)。为了衡量引入算法的性能，对几个设计良好的测试函数进行了数值分析，并将结果与​​原始 GSA 和其他基于进化的算法进行了比较。