Cooperative  metaheuristic algorithm for global optimization and engineering problems inspired by heterosis theory

Swarm Intelligence-based metaheuristic algorithms (MAs) are widely applied to global optimization and engineering design problems. However, these algorithms often suffer from two main drawbacks: susceptibility to the local optima in large search spaces and slow convergence rates. To address these issues, this paper develops a novel cooperative metaheuristic algorithm (CMA) inspired by Heterosis theory. Firstly, the CMA population is divided into three subpopulations for cooperative evolution. Subsequently, the Particle Swarm Optimization algorithm is employed to conduct global and local searches. To enhance global search capability and prevent the algorithm from getting trapped in the local optima, Levy flights are utilized. For fine-tuning local searches, the Ant Colony Optimization algorithm is applied, utilizing path exploration and pheromone updates to guide and refine the search process. Then, the global optima of the three subpopulations are shared through a ring information exchange mechanism, combined with an elite attribute-based learning strategy to accelerate convergence. Additionally, to ensure population stability and diversity, a similarity-based strategy is used to dynamically adjust the communication frequency among subpopulations, facilitating smooth transitions between exploration and exploitation. To validate the effectiveness of CMA, this study evaluates the algorithm using 26 well-known benchmark functions and 5 real-world engineering problems. Experimental results demonstrate that CMA outperforms the 10 state-of-the-art algorithms evaluated in the study.