Multi-optimum fuzzy programming based particle swarm optimization

In particle swarm optimization, although falling into local optimums can be avoided by introducing the information of multi-optimum distribution state into the particle swarm movement, the performance of the algorithm is limited because the programming proportion factor of multi-optimum cannot be dynamically adjusted in the optimization process. A kind of fuzzy strategy based on double-variable and single-dimensional fuzzy control structure is proposed and used to the dynamic programming of particle swarm multi-optimum. Simulation results show that this kind of fuzzy multi-optimum programming mode has better general convergence performance than traditional PSO algorithm and the static multi-optimum programming mode.