改进的自适应粒子群优化算法  

Improved adaptive Particle Swarm Optimization algorithm

作　　者：李蓉[1] 沈云波[1] 刘坚[1] 

机构地区：[1]湖南大学汽车车身先进设计制造国家重点实验室,长沙410082

出　　处：《计算机工程与应用》2015年第13期31-36,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.71271078);国家科技重大专项(No.2011ZX04015-041);长沙市科技计划项目(No.k1307024-31);湖南大学"中央高校基本科研业务费"资助项目

摘　　要：提出了一种融合梯度搜索法、繁殖法并结合前N个粒子历史最优位置的改进自适应粒子群优化算法。算法选用混沌惯性权重,每个粒子速度和位置的更新不仅考虑自身历史最优和全局最优位置,还受其他粒子历史最优位置的影响,且其影响程度的权重随迭代次数自适应变化;同时粒子位置随迭代次数以线性递增的概率进行负梯度方向更新;当粒子更新停滞时,对可能处于局部最优位置的部分粒子进行杂交。仿真实验结果表明,该算法比其他相关算法具有更好的收敛速度和收敛精度。The paper proposes an improved adaptive Particle Swarm Optimization（PSO）algorithm which integrates gradient search method,breeding method and the all-time optimal location information of the first N particles.With chaotic inertia weight,the renewal of each particle＇s speed and position is considered with not only the information of its own all-time and global optimal location information,but also the information of other all-time optimal location information,while the weights of other particles＇all-time optimal location information change adaptively with the number of iterations;meanwhile,particles location updates in their negative gradient direction and the particle locations increase linearly with iterations; when the particles stop updating,cross may be hold in local optimum position.The experimental results verify that the algorithm has better convergence speed and convergence precision than those relevant algorithms.

关 键 词：粒子群优化算法 梯度搜索 繁殖法 自适应 惯性权重 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]