Multi-population Cooperative Particle Swarm Optimization with Covariance Guidance

The particle swarm optimization algorithm has been widely utilized to address a wide range of different engineering optimization problems due to its few parameters and simple structure. The conventional particle swarm optimization algorithm takes in information from two sources: Global optimal particle and individual optimal particle. However, learning from these two sources alone is inefficient to solve complex high-dimensional problems. Therefore, this paper proposes a multi-population cooperative particle swarm optimization (COVPSO) algorithm with covariance guidance strategy, through the use of a covariance guidance strategy to guide population evolution direction. In COVPSO, the population is divided based on the Euclidean distance from the particle to the global optimal particle, and the population is divided into the elite group, exploratory group, and inferior group. As a result of grouping the population and adopting different strategies, the elite group has good exploitation ability, while the exploration group has good exploration ability, and the inferior groups by introducing a differential mutation operator to improve global exploration ability. Therefore, the population has a good balance between exploration and exploitation. This study utilizes ten benchmark functions and five PSO variants broadly used in the literature to verify the merits of COVPSO to demonstrate its efficiency. The findings of the experiments show that COVPSO has a faster convergence rate and a more precise solution.