The Application of Particle Swarm Optimization Algorithm in the Extremum Optimization of Nonlinear Function
Jin-yue Liu, Bao-ling Zhu · 2012
For the non-linear function extremum optimization, this paper draws on the ideology of mutation in genetic algorithm and introduces the mutation operation in the standard particle swarm algorithm to increase the possibility of the algorithm to search the optimal value, the LDWPSO(linearly decreasing weight particle swarm optimization) is adopted to balance the global search and local search ability of the algorithm. By the optimization test for the multi-peak function, the improved algorithm is compared with the standard particle swarm optimization, which demonstrates that the former one owns better global optimization ability and higher convergence rate.