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.

Key words: particle swarm optimization, strengthen learning, associative memory, chasing the best and avoiding the worst, simulation test

摘要： 为了克服粒子群优化算法多维搜索时方向性差、目的性弱以及易早熟收敛等缺点，提出了一种改进的粒子群优化算法。改进的算法分别对认知部分及社会部分的最优信息、最差信息赋予不同的学习因子，使算法具有更强的学习能力。每个粒子联想记忆其历史最优、最差信息，然后按照追逐最优躲避最差的原则寻找最优位置。联想记忆克服了多维搜索中方向性差、目的性弱的缺点；追优避差保持了种群的多样性，有利于提高算法的收敛速度、克服早熟收敛。通过基准函数的仿真测实验证了算法的有效性。

关键词: 粒子群优化, 加强学习, 联想记忆, 追优避差, 仿真测试