摘要
群体智能是通过模拟自然界生物群体行为来实现人工智能的一种方法,它强调个体行为的简单性,群体的涌现特性,以及自下而上的研究策略.结合群体智能的优点,设计了利用信息共享进行子群体迁徙策略和信息浓度更新规则,实现了各子群体之间的协同,避免了各子群体进行封闭竞争,加快了各子群体的收敛速度,使得在较好信息的利用和全局性的搜索两个方面达到最佳平衡,加快搜索速度的同时保证全局收敛.通过4个测试函数的优化证明了算法的可行性和高效性.应用于自适应在线整定PID控制参数,获得了良好的控制效果.
Swarm intelligence （SI） is a kind of method to realize artificial intelligence by simulating biological colony behaviors in nature. It emphasizes the simplicity of individual behavior, group stigmergy and down-up research strategy. The advantages of swarm intelligence are analyzed. According to social information share, the group migration strategy is designed and refreshing rule of information density is presented, which realizes groups＇ cooperation, avoids close competition and accelerates convergent velocity. The strategy achieves the best balance between better exploiting and global exploring, enhances the searching velocity and attains global convergence. Optimum experiments by four testing functions show that the improved MEA is feasible and efficient. The improved MEA are used to on-line adjust PID parameters and better control performances are obtained.
出处
《中北大学学报（自然科学版）》
CAS
北大核心
2011年第3期303-308,共6页
Journal of North University of China(Natural Science Edition)
基金
国家自然科学基金资助项目(60843006)
高等学校博士学科点专项科研基金项目(2006112005)
山西省自然科学基金资助项目(2008011027-4)
关键词
子群体迁徙
思维进化算法
群体智能
信息浓度
group migration
mind evolutionary algorithm （MEA）
swarm intelligence
informationdensity
