作　　者： ; ; ;

机构地区： 湖南大学电气与信息工程学院

出　　处： 《控制与决策》 2007年第9期1027-1031,1043,共6页

摘　　要： 针对混沌优化对初始值敏感、搜索精确解效率低等不足,研究一种基于竞争-协作式信息交互的并行混沌优化(ICPCO)算法.ICPCO算法采取并行混沌迭代机制,在每一次迭代搜索之后,根据并行优化解分布状况不同,分别采取竞争或协作式信息交互再次寻优.描述ICPCO算法思想和实现步骤,分析其收敛性和优化性能.仿真实验表明,ICPCO算法不仅具有全局搜索能力,而且以信息交互方式提高了优化效率和搜索精度,算法收敛,稳定性增强. For the problem that chaotic optimization is sensitive to initial values and suffers from slow convergence velocity around optimum and poor efficiency for accurate optimum, a parallel chaotic optimization algorithm based on competitive/cooperative inter-communication （ICPCO） is proposed. In the proposed algorithm, each variable is mapped to several chaotic variables called parallel chaotic system, and competitive/cooperative inter-communication between parallel variables is employed for research in accordnce with distributing-status of parallel variables. Both framework and algorithmic implementation of the proposed algorithm are introduced. Optimization performance and convergence of ICPCO are analyzed. Simulations demonstrate that this algorithm has better performance over other parallel chaotic optimization.

关 键 词： 计算智能 混沌 并行混沌优化 信息交互 全局搜索

领　　域： [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术]