Modified adaptive particle swarm optimization algorithm based on probabilistic leap and simulated annealing

On the basis of analyzing two particle swarm optimization (PSO) algorithms, the standard PSO(SPSO) and self-adapting PSO(SAPSO), a modified adapting PSO(MAPSO) algorithm is proposed to solve the problem that PSO may trap to local optimum and fluctuation during later period. In this algorithm, the probabilistic leap factor is introduced to modify the velocity updating and the acceptable rule of simulated annealing is applied to restrain the uncontrollability of probabilistic leap. The results of typical optimization show that this algorithm has better accuracy and convergence rate as well as fewer iteration numbers in approaching the global optimization than SPSO and SAPSO algorithms. This algorithm is also superior to SPSO and SAPSO algorithms in stability and ability of breaking off local search.