A High-Performance Learning Particle Swarm Optimization Based on the Knowledge of Individuals for Large-Scale Problems

To improve the performance of particle swarm optimization in solving large-scale problems, a High-Performance Learning Particle Swarm Optimization (HPLPSO) based on the knowledge of individuals is proposed. In HPLPSO, two strategies are designed to balance global exploration and local exploitation according to the principle of symmetry, which emphasizes balance and consistency during the optimization process. A strategy for elite individuals to guide population updates is proposed to reduce the impact of local optimal positions. Meanwhile, a synchronous opposition-based learning strategy for multiple elite and poor individuals in the current iteration population is proposed to help individuals quickly jump out of the non-ideal search areas. Based on classical test functions for large-scale problems, HPLPSO performance is tested in 100, 200, 500 and 1000 dimensions. The results show that HPLPSO can converge to the theoretical optimal value in each of its 30 independent runs in 11 functions. Moreover, the values of mean variation from dimension 100 to 1000 present that HPLPSO is little affected by dimensional changes. The case application further validates the performance of the algorithm in solving practical problems. Therefore, the paper provides a method with high optimization performance to solve large-scale problems.