具有自适应弹射机制的粒子群算法

针对粒子群算法容易陷入局部最优和停滞的问题,提出自适应弹射机制的粒子群算法.为了保持粒子群的活力,在算法内引入弹射操作.当粒子满足条件,当前位置赋予很大的速度,使其飞到很远的区域.弹射方式可以选择全维弹射和概率弹射.为了配合弹射操作,提出粒子优劣的判断机制,使粒子可以被弹射飞出可行域.在算法中设定自适应判别函数,当粒子满足该判别函数,对粒子实施弹射.数值实验表明,文中算法具有较强的全局搜索能力和较快的搜索速度.

Self-adaptive Ejector Particle Swarm Optimization Algorithm

Particle swarm optimization(PSO) is easily trapped in local optimum and stagnation, and therefore a self-adaptive ejector particle swarm optimization algorithm(SAEPSO) is proposed. To keep the vitality of the particle swarm, the ejector operation is introduced into the algorithm. While the satisfying the condition, the particle is given a high speed at the current position to fly to a faraway area. Full-dimensional ejection and probabilistic ejection can be selected for the ejection mode. To cope with the ejector operation, a new quality judgment for particles is proposed, so particles can be ejected out of the feasible region. A self-adaptive discrimination function is introduced in the proposed algorithm to judge whether the particle should be ejected. While satisfying the function, the particles are ejected. Numerical experiments show that the proposed algorithm possesses relatively strong global search ability and fast search speed.