Adaptive Multi-Region Multi-Directional Competitive Swarm Optimizer Algorithm for Large-Scale Multi-Objective Problems

Competitive swarm optimization has shown impressive performance in addressing large-scale multi-objective optimization problems. However, its competitive mechanism can lead to contention among local optimal solutions within the current population. As the decision space grows more complex, the diversity of the population in competitive swarm optimization often decreases, resulting in “premature convergence”. To address this issue, we propose an adaptive multi-region multi-directional competitive swarm optimization algorithm, named AMRCSO. First, this algorithm introduces an adaptive reference-guided subregion division strategy that segments the objective space based on the population’s distribution, enhancing search diversity and coverage. Additionally, a multi-directional exploration strategy is employed to explore each subregion of the decision space, enabling parallel and multi-directional exploration that accelerates convergence to the global optimal solution. Furthermore, an effective population initialization and tri-stage updating strategy are implemented to improve population diversity and enhance convergence accuracy, respectively. Experimental results on large-scale multi-objective benchmark functions, ranging from 500 to 5000 decision variables, demonstrate that the AMRCSO algorithm achieves superior convergence accuracy and distribution compared to existing algorithms for large-scale multi-objective optimization problems.