改进的粒子群优化算法

提出了改进的粒子群优化算法。基于4个不同的基准函数对所提算法与1995年Kennedy和Eberhart提出的常规PSO作了比较。PSO最初是受到如鸟或鱼等生物群体的社会行为的启发而提出的,每一个体依照自身及群体的过去解决问题的最好办法来调整自己的最佳位置,通过重复这一过程来得出最佳值。这里提出的改进的PSO的关健之处在于:如果一个新的位置确实得到了改善,则每一个体就调整它的位置;如果不是这样,就根据概率来做出决定。这一策略是既避免盲目跳转又避免只简单地跳转到好的新位置而陷入局部最优。模拟结果表明改进的PSO总能比PSO找到更好的解决方法。

A Modified Particle Swarm Optimization Technique

In this paper,a modified particle swarm optimization method is proposed.It is compared with the regular particle swarm optimizer(PSO) invented by Kennedy and Eberhart in 1995 based on four different benchmark functions.PSO is motivated by the social behavior of organisms,such as bird flocking and fish schooling.Each particle studies its own previous best solution to the optimization problem,and its group's previous best,and then adjusts its position(solution) accordingly.The optimal value will be found by repeating this process.In the modified PSO proposed here,each particle adjusts its position if the new position improves but makes a decision by some probabilities if it does not.The strategy here is to avoid simply jumping into new position no matter how bad it is.Under all test cases,simulation shows that the modified PSO always finds better solutions than PSO.