MCPSO-ALS: A Multi-Swarm Collaborative Particle Swarm Optimization with Adaptive Learning Strategy for Solving Global Optimization Problems

Particle swarm optimization (PSO) is a classical metaheuristic algorithm that has been widely used to solve continuous optimization problems. However, it still suffers from inherent drawbacks, such as being prone to premature convergence and an imbalance between exploration and exploitation. To address these problems, a Multi-swarm Collaborative Particle Swarm Optimization with Adaptive Learning Strategy (MCPSO-ALS) is proposed in this paper. Firstly, an Adaptive Population Division Strategy (APDS) is designed to realize effective information interaction among particles. At each iteration, the overall population is dynamically divided and reorganized, based on the fitness values of all the particles. Specifically, particles with high fitness form an elite population focusing on local search; particles with low fitness form a poor population focusing on global search; and ordinary particles form a general population aiming to achieve a balance between exploration and exploitation. Secondly, an Adaptive Learning Mechanism (ALM) is introduced. In this mechanism, particles with different roles adopt distinct updating strategies, which productively guarantees the dynamic equilibrium between exploration and exploitation. To validate the outstanding performance of the proposed algorithm, comprehensive experiments are conducted on CEC2013 and CEC2017. The experimental results demonstrate that the proposed method exhibits significant advantages in convergence speed, solving accuracy, and comprehensive optimization capabilities compared with several state-of-the-art algorithms.