An Advanced Adaptive Group Learning Particle Swarm Optimization Algorithm

The particle swarm optimization algorithm, known for its swift convergence and minimal parameters, is widely used to solve various complex optimization problems. Despite its merits, it encounters challenges such as susceptibility to premature convergence and reduced search capability in later stages. To improve the optimization performance of the algorithm, this study designs an advanced adaptive Group Learning Particle Swarm Optimization (GLPSO) algorithm based on the concept of symmetry. Firstly, a new coefficient is proposed to enable rapid convergence of the algorithm, allowing the population to quickly converge to the optimal position. By adopting a group learning mechanism, the optimal particles in each group are learned during particle updates; this enhances the diversity of particle learning in the algorithm, improving its global search capability. A novel adaptive perturbation rule is proposed, enabling particles to perform perturbations in a more suitable manner, thereby enhancing the population’s ability to escape local optima. GLPSO is compared with several advanced algorithms on internationally recognized high-dimensional benchmark functions and is also applied to some constrained engineering problems. The results indicate that GLPSO outperforms other improved algorithms on most test problems and provides competitive and high-quality solutions for engineering problems. Overall, GLPSO enhances its optimization capability while maintaining robustness.