摘要
将基本粒子群算法粒子行为基于个体极值点和全局极值点变化为基于个体极值中心,并且按一定概率选择其他粒子的个体极值点,设计了一种新的粒子群优化算法。新算法的学习行为符合自然界生物的学习规律,更有利于粒子发现问题的全局最优解。实验结果表明了算法的有效性。
A new particle swarm optimization algorithm was presented in the paper by altering particle behaviour from individual extremum point and global extrema points based to individual extremum centre based in fundamental particle swarm algorithm and by selecting the indi- vidual extremum point of other particles in a certain probability. The learning behaviour of the new algorithm accords with the learning law of natural living beings and is beneficial to global optimal solution for particle discovery problems. The testing results also showed the validity of the algorithm.
出处
《计算机应用与软件》
CSCD
北大核心
2008年第5期85-86,111,共3页
Computer Applications and Software
基金
陕西省教育厅专项基金资助项目(05JK303)。
关键词
粒子群优化算法
群智能
进化计算
Particle swarm optimization algorithm Swarm intelligence Evolutionary computation
