Adaptive Weight Particle Swarm Optimization Algorithm with Constriction Coefficient
Zhiyu You · Journal of Southwest University · 2011
In global optimization process,common particle swarm optimization algorithm can easily fall into the local optimization arising from the phenomenon of prematurity.Through an analysis of its convergence,this paper presents an adaptive weight particle swarm optimization algorithm with the compression coefficient.The algorithm changes the value of the objective function for the information to change the value,so that the algorithm may reach an effective balance between global optimization and local exploration.Four Benchmark functions are used to test the performance of the optimization algorithm,and the results show that compared to other improved particle swarm algorithms,this algorithm can effectively overcome prematurity to some extent in the process of global optimization.