An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm

Abstract

To solve the shortcomings of Particle Swarm Optimization (PSO) algorithm, local optimization and slow convergence, an Opposition-based Learning Adaptive Chaotic PSO (LCPSO) algorithm was presented. The chaotic elite opposition-based learning process was applied to initialize the entire population, which enhanced the quality of the initial individuals and the population diversity, made the initial individuals distribute in the better quality areas, and accelerated the search efficiency of the algorithm. The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm, and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum. The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics, and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability, search accuracy and convergence speed. In addition, the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.