Elite Guided Jaya Algorithm with Mutation Strategy for Global Optimization and Parameter Extraction

During the development of metaheuristic algorithms, many classic algorithms have emerged, and the Jaya algorithm (JAYA) is one of them. The inspiration of JAYA comes from the idea that the population should stay away from the worst solution already discovered during the search process, and should approach the best solution already discovered. Although JAYA demonstrates excellent performance in solving simple problems, it is prone to getting stuck in local optima when solving complex problems due to its reliance on the worst and best solutions already discovered. In order to improve the performance of JAYA, this paper proposes an improved JAYA, namely an elite guided Jaya algorithm with mutation strategy (EDJAYA). EDJAYA adopts an elite population driven mechanism to optimize its search strategy, and designs a mutation strategy to enhance its ability to escape from local optima. The excellent optimization performance of EDJAYA has been verified through solving numerical problems and practical problems of photovoltaic model parameter estimation.