Abstract: Aiming at the problems of slow convergence and premature convergence of particle swarm optimization (PSO), a PSO algorithm based on variable-scale black hole and population migration, named IRBHPSO, is proposed. The variable-scale black hole is introduced to balance the weight of the global exploration and local optimization of the algorithm. The displacement coefficient based on the hybrid strategy is introduced into the position update strategy to enhance the convergence speed of the algorithm in the early iteration and the local optimization ability in the later iteration. The Butterfly Optimization Algorithm (BOA) based on population migration is integrated into PSO as a local operator to improve the problem that PSO has slow convergence speed and is easy to fall into local optimum. IRBHPSO, PSO, and other related algorithms are simulated on 12 benchmark test functions, and Wilcoxon rank sum test is performed. The results show that IRBHPSO has better convergence accuracy, convergence speed and stability.

Key words: particle swarm optimization algorithm, variable-scale black hole, displacement coefficient, butterfly optimization algorithm, population migration ,  ,