A Multi-Strategy Improved Golden Jackal Optimization Algorithm Integrating the Golden Sine Mechanism

In response to the shortcomings of poor population diversity, weak global search ability, and susceptibility to local optima in the Golden Jackal Optimization Algorithm, this paper proposes a Multi-Strategy Improvement GJO (MSIGJO) algorithm that integrates the Golden Sine mechanism. Firstly, Latin hypercube sampling is used to initialize the golden jackal population, improving the quality of initial solutions. Secondly, by incorporating the golden sine mechanism as an operator into the search stage of the Golden Jackal algorithm, the optimization accuracy of the algorithm is improved. Finally, the adaptive t-distribution is used to perturb the optimal individual adaptively, and greedy strategies are employed to find the optimal solution. The comparison test results of MSIGJO and five other intelligent algorithms through 8 benchmark test functions show that the improved algorithm in this paper is superior to different algorithms in terms of convergence speed and optimization.