Abstract：In order to overcome the weakness of direction and the poorness of purpose in multidimensional search and the premature convergence, this paper presented an improved particle swarm optimization algorithm. For both the best and the worst information of the cognitive part and the best and the worst information of the social part, the improved algorithm respectively assigned different learning factors, and the algorithm has a greater ability to learn. Each particle associatively memorized the best information and the worst information in its history, and then found the optimal position in accordance with the principle of chasing the best and avoiding the worst. Associative memory overcomes the weakness of direction and the poorness of purpose in multidimensional search. The principle of chasing the best and avoiding the worst keeps the diversity of population, helps to improve the convergence speed, and overcomes the premature convergence. Simulation test of the benchmark function has verified the validity of the algorithm.
2012, Vol. 32 
