云自适应粒子群算法

Adaptive particle swarm optimization algorithm based on cloud theory

文中提出了云自适应粒子群优化(CAPSO)算法,根据粒子适应度值把种群分为三个子群,分别采用不同的惯性权重生成策略,由X条件云发生器自适应调整普通子群粒子的惯性权重,由于云模型云滴具有随机性和稳定倾向性特点,使惯性权重既具有传统的趋势性,满足快速寻优能力,又具有随机性,在提高收敛速度和保持种群多样性之间做了一个很好的权衡。通过典型函数优化实验表明,与标准粒子群算法相比,CAPSO具有较高的计算精度和较快的收敛速度。

In this paper,an adaptive particle swarm optimization algorithm based on cloud theory is proposed,the particles are divided into three group based on the fitness of the particle in order to adopt different inertia weight generating strategy.The inertia weight in general group is adaptively varied depending on X-conditional cloud generator.The inertia weight has the stable tendency and randomness property because of the cloud model,this not only improves the convergence speed,but also maintains the diversity of the population.In all cases studied,CAPSO is greatly superior than PSO in the terms of efficiency.