基于Tent映射的混沌混合粒子群优化算法  

Chaotic hybrid particle swarm optimization algorithm based on Tent map

作　　者：程志刚[1] 张立庆[2] 李小林[2] 吴晓华[3] 

机构地区：[1]浙江科技学院信息与电子工程学院,浙江杭州310012 [2]浙江科技学院生物与化学工程学院,浙江杭州310012 [3]浙江大学智能信息工程研究所,浙江杭州310027

出　　处：《系统工程与电子技术》2007年第1期103-106,共4页Systems Engineering and Electronics

基　　金：浙江省自然科学基金(Y404082);浙江省教育厅重点科研计划项目(20030836)资助课题

摘　　要：为改善基本粒子群优化算法的寻优性能,通过算法混合,在粒子群优化算法中逐步引入优进策略和混沌搜索机制,以加强粒子群的局部寻优效率和全局寻优性能。并将粒子分为两类,分别执行不同的进化机制,实现协同寻优,从而构建为一种新的混沌混合粒子群优化算法。标准测试函数的仿真优化结果表明,该混合算法对较大规模的复杂问题具有较强的求解能力。算法寻优效率高、全局性能好、优化结果稳定,性能明显优于标准粒子群优化算法以及遗传算法等单一的随机搜索方法。Aiming to improve the performance of standard particle swarm optimization algorithm, a new method-chaotic hybrid particle swarm optimization （CHPSO） algorithm is introduced through the technique of algorithm hybrid. By integrating eugenic strategy and chaotic optimization into particle swarm optimization algorithm, it greatly enhances the local searching efficiency and global searching performance. Furthermore, the particles are divided into two classes and perform different operations to co-evolve. Simulation results on standard test functions show that CHPSO is pretty efficient to solve high dimensional complex problems. It has high optimization efficiency, good global performance, and stable optimization outeomes. The performance of CHPSO is evidently better than PSO and SGA.

关 键 词：优化算法 优进策略 混沌 TENT映射 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]