Abstract: The particle swarm optimization (PSO) algorithm often suffers from problems such as low population diversity and being trapped in local optimal solutions. To address these issues, a particle swarm optimization algorithm with centroid opposition based learning and simplex search (COLS-PSO) is proposed. During the initialization process, the search space is constructed based on a chaos strategy. During the evolution process, the particles that need to undergo centroid opposition-based learning are selected based on the Spearman coefficient to help the algorithm escape from local extreme value areas. Furthermore, a simplex search method with strong local search ability is introduced to enhance the development of the optimal particle's neighboring area and improve the search accuracy. The algorithm is tested on several standard test functions and then applied to software testing data generation problems. The experimental results show that the COLS-PSO algorithm performs well in terms of solution accuracy, convergence speed, and effectiveness, and can effectively balance the contradiction between population diversity and algorithm convergence. 

Key words: particle swarm optimization (PSO) algorithm, chaotic strategy, centroid opposition-based learning, simplex search, test data generation