改进的云自适应粒子群优化算法

Improved adaptive particle swarm optimization algorithm based on cloud theory

为了提高基本PSO算法搜索性能和个体寻优能力,加快收敛速度,提出一种新的云自适应粒子群优化算法（CPSO）。此算法利用云滴具有随机性、稳定倾向性等特点,结合不同粒子与全局最优点的距离动态变化的性质,提出云自适应调整算法用于计算惯性权重,并对新算法进行了描述。通过典型函数优化实验表明,该算法较基本PSO明显提高了全局搜索能力和收敛速度,改善了优化性能。

For the purpose of improving the basic PSO’s search performance and individual optimizing ability,speeding up the convergence,presented an adaptive particle swarm optimization based on cloud theory （ CPSO） ,relative to the basic PSO algorithm. The inertia weight was adaptively varied depending on X-conditional cloud generator. The inertia weight had the stable tendency and randomness property because of the cloud model and the distance between the particle and the current optimal position. Experimental results show CPSO can greatly improve the global convergence ability and enhance the rate of convergence.