摘要
针对多峰函数优化问题,借鉴粒子群优化特性和免疫网络理论,提出一种免疫粒子群网络算法。该算法利用粒子群的信息共享和记忆功能,通过加强粒子对自身经历的认知,提高算法的局部搜索能力;采用动态网络抑制策略,保持种群的多样性,自适应地调节粒子群的规模。多峰函数优化的仿真结果表明,该算法能有效地改善种群的多样性,较好地实现全局优化和局部优化的有机结合,具有更强的多峰函数优化能力。
Referred to the character of particle swarm optimization and immune network theory, an immune particle swarm network algorithm for multimodal function optimization is proposed. By making use of the information sharing and memory function of particle swarm, the cognitive part based on its own experience has been enhanced to improve local searching ability of the algorithm. The strategy of dynamic network suppression has been used to maintain diversity of population, and adjust adaptively the scale of particle swarm. Simulation results of typical test functions show the algorithm can not only improve population diversity effectively, but realize the combination of global optimization and local optimization well, thus has excellent optimization performance.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第3期705-709,共5页
Systems Engineering and Electronics
关键词
多峰优化
粒子群优化
免疫网络
局部优化
multimodal optimization
particle swarm optimization
immune network
local optimization
