摘要
由于模式搜索算法易陷入局部极值且效率低.受群智能算法的启发,结合模式搜索算法,提出一种全局优化算法——群模式全局搜索算法.该算法引入群智能的思想,包含4个操作:模式探测、模式移动、模式学习、模式扩散,具有较强的局部-全局搜索能力,且收敛速度快、稳定性好.对benchmark函数集进行仿真并与其它多个算法对比,实验结果证实该算法的有效性.
Pattern search algorithm often falls into local optimization and its efficiency is low. Inspired by swarm intelligence algorithm, a global optimization algorithm, swarm pattern global search algorithm （SPGSA）, is proposed. Swarm intelligence is introduced to SPGSA in the evolution process. Thus, SPGSA includes pattern search operator, pattern moving operator, pattern learning operator and pattern dispersion operator. It has a strong ability of global and local search as well as better features of fast convergence and good stability. Comparisons of functions prove the effectiveness. the simulation results by using standard benchmark
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第6期592-597,共6页
Pattern Recognition and Artificial Intelligence
基金
广西自然科学基金项目(No.2010GXNSFB013052)
广西科学研究与技术开发项目(No.桂科攻11107006-30)
广西重点实验室科研项目(No.HCIC201105)
广西高等学校科研项目(No.201204LX085)资助
关键词
群模式搜索算法
局部优化
全局优化
群智能
Swarm Pattern Search Algorithm, Local Optimization, Global Optimization, SwarmIntelligence
