Abstract: Aiming at the problems of poor convergence accuracy, easy to fall into local optimality of golden jackal optimization algorithm when solving optimization problems, an elite golden jackal optimization algorithm based on multi-strategy (EGJO) is proposed. Firstly, the elite opposition-based learning is used to select the elite population to find the optimal solution, that can improve the quality and diversity of the population,  thus  effectively improve the convergence accuracy and speed of the algorithm. Secondly, the two-sided mirror reflection theory is used to deal with the transboundary individuals to solve the problem of uneven population distribution. Thirdly, an adaptive energy factor is proposed to better coordinate the exploration and the exploitation. Finally, Cauchy mutation strategy is applied to the optimal individuals of the population to improve the ability of the algorithm to jump out of the local optimal. Through the optimization simulation experiment of 16 typical benchmark functions, the convergence, robustness, Wilcoxon rank sum test and other aspects are analyzed comprehensively, and the six optimization algorithms are compared. The experimental results show that the convergence accuracy and speed of the EGJO are significantly improved. In addition, two typical engineering problems are optimized, and the results show that the proposed algorithm has the feasibility and efficiency to solve the actual optimization problems.

Key words: golden jackal optimization algorithm, elite opposition-based learning, adaptive energy factor, boundary processing, rank sum test, engineering optimization