An Experience-Exchange Learning Golden Jackal Optimization Algorithm for High-Dimensional Optimization Problems

The golden jackal optimization (GJO) algorithm is an emerging swarm intelligence method inspired by the remarkable hunting strategies of golden jackals in their natural habitat. We propose an experience-exchange learning GJO (ELGJO) based on experience-exchange learning, aiming to address the challenges posed by high-dimensional optimization problems. By incorporating the experience-exchange learning mechanism, individuals can more effectively harness collective intelligence, leading to an enhanced ability to avoid getting trapped in local optima and potentially improving the search for global optimal solutions. Moreover, cauchy perturbation strategy is introduced to enhance local search capability. The proposed algorithm is applied to a series of high-dimensional benchmark functions. The experimental results show that our proposed ELGJO has better performance than the other four comparison algorithms in high-dimensional optimization problems.