摘要
为了保持群体多样性以增强全局搜索能力,小生境技术在遗传算法中得到了广泛应用.针对多模态函数优化问题,将小生境技术引入到粒子群算法中,建立小生境熵作为群体多样性的量化指标,实时考查进化过程中群体的多样性并调整进化参数;结合数论中的佳点理论,提出一种在解空间使用佳点搜索的群体多样性发掘方法,使得进化过程中群体多样性水平始终保持在设定的阈值之上,从而改善算法的全局搜索能力以期跳出局部最优;在此基础上提出一种旨在找出全部全局最优解和局部最优解的新型串行多群体小生境粒子群算法.数值实验表明,改进的小生境粒子群算法在求解多模态函数优化问题时具有较好的自适应性和收敛性.将算法应用于图像配准实验中,使得配准参数估计误差有明显降低.
For maintaining the population diversity, niche technique was widely used in genetic algorithm to enhance the global search capability. In this paper, the niche entropy was introduced to measure the diversity of the population on the basis of an adaptive niche identification approach and the evolutionary parameters of the PSO algorithm can be adjusted adaptively according to the niche entropy of population. Moreover, an effective approach to explore new schemas in search space is designed, which makes the diversity level of population always higher than the threshold value set beforehand during the evolutionary process, so the proposed algorithm can obtain strong ability to search out the global optimal solutions. And a novel sequential multi-population niche＇ PSO algorithm which aims at find out all the global and local optimal solutions is proposed as a result. Experiments for the multimodal function optimization show that the proposed algorithm has strong adaptability and convergence.
出处
《小型微型计算机系统》
CSCD
北大核心
2011年第9期1854-1861,共8页
Journal of Chinese Computer Systems
基金
国家科技重大专项项目(2009ZX04009-022)资助
