摘要
将一定规模的粒子群平分成三个子群,并分别按基本粒子优化算法、ω自线性调整策略的粒子群算法和云自适应粒子群算法三种不同规则进化,既保持各个子群和算法的独立性和优越性,又不增加算法的复杂性,并提出"超社会"部分,重新定义了速度更换式子,同时还引入了扩张变异方法和扰动操作。实验仿真结果表明,给出算法的全局搜索能力、收敛速度,精度和稳定性均有了显著提高。
A certain number particle swarm is subdivided into three equal groups,then they evolve with different rules, which are particle swarm optimizer,a modified particle swam optimizer and a cloud adaptive particle swarm optimizer.It not only keeps the independence of the particle swarm and the superiority of the optimizer,but also not increases the complexity of algorithm.Meanwhile,the super-society parts are put forward and then the velocity formula is redefined.At the same time,variation expansion methods and disturbance operation are introduced.The experimental results show that the new algorithm has advantages of convergence property,convergence speed,accuracy and the stability of convergence effective.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第19期51-54,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.60461001)
广西自然科学基金(No.0832082)~~
关键词
粒子群优化算法
多种群
云理论
扩张变异方法
超社会部分
particle swarm optimization
multi-colony
cloud theory
variation expansion methods
super-society parts
