An Improved Particle Swarm Optimization with an Adaptive Updating Mechanism

Premature convergence when solving multimodal problems is still the main limitation which affects the performance of the PSO. To avoid of premature, an improved PSO algorithm with an adaptive updating mechanism (IPSO) is proposed in this paper. When the algorithm converges to a local optimum, the updating mechanism begins to work so that the stagnated algorithm obtains energy for optimization. That is, the updating mechanism refreshes the swarm and expands the range for exploration. In this way, the algorithm can achieve a good balance between global exploration and local exploitation by the combination of the basic PSO evolution and updating mechanism. The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through other 4 existing PSO algorithms. The simulation results elucidate that the proposed method produces the near global optimal solution, especially for those complex multimodal functions whose solution is difficult to be found by the other 4 algorithms. It is also observed from the comparison the IPSO is capable of producing a quality of optimal solution with faster rate.