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.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
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
