云模型改进惯性权重的混沌交替粒子群算法

标准粒子群算法通过线性减小惯性权重系数来调整寻优性能,但缺乏智能化机制易导致算法后期产生早熟或陷入局部最优而产生僵局。针对这一问题,提出一种基于云模型改进惯性权重的混沌交替粒子群优化算法。根据粒子迭代变化关系,采用云模型理论对惯性权重ω进行智能化调整,以平衡其全局和局部搜索能力,防止算法产生局部僵局;另外,判定粒子稳定性,对于可能陷入局部僵局的稳定粒子进行混沌扰动,促使其跳出僵局进而向最优位置更新。实验与分析表明,基于云模型改进惯性权重的混沌交替粒子群优化算法能够跳出局部僵局且具有较高的寻优精度,算法接近完全收敛时的平均迭代次数,较现有相关研究分别降低了13.73%~20.11%。

Chaos Alternation Particle Swarm Optimization Algorithm Improved Inertia Weight Based on Cloud Model

The optimization performance of the standard particle swarm optimization algorithm is adjusted by reducing the inertia weightlinear,which lack of intelligent mechanism and easy to bring into the prematurity and local stalemate in the evening of the algorithm. A chaos alternation particle swarm optimization algorithm improved the inertia weight based on cloud model is proposed to solve these problems. The inertia weight ω of the particle swarm optimization algorithm is adjusted by cloud model intelligently according to the iterative transformation of the particles,and the whole and local searching capabilities of the particle swarm optimization algorithm get balanced,and prevent it into the local stalemate. In addition,determine the stability of the particles,and do chaos disturbance to the stable particles which maybe bring into the local stalemate,make it jump out form the local stalemate and close to the optimization position further. It shows from the experiment and analysis that the chaos alternation particle swarm optimization algorithm improved the inertia weight based on cloud model can be able to jump out from the local stalemate with a higher optimization precision,and the average iterative numbers are reduced by 13.73 %~20.11 % than other researches when the algorithm gets absolutely convergence.