Particle Swarm Optimization algorithm with abundant social cognition
Dingfang Li · Computer Engineering and Applications Journal · 2009
A particle swarm optimization with abundant social cognition is developed for solving premature convergence of particle swarm optimization.In this algorithm,the optimum from the particles experiments is determined by learning probability and selective probability.The learning probability is adjusted to balance between personal cognitive and social cognitive.Experimental results for complex function optimization show this algorithm improves the global convergence ability and efficiently prevents the algorithm from the local optimization and early maturation.