蚁群参数自适应调整的优化设计  

Ant colony optimization with self-adaptive parameter adjusting

作　　者：刘道华[1] 熊炎[1] 李为华[1] 李湘英[1] 

机构地区：[1]信阳师范学院计算机与信息技术学院,河南信阳464000

出　　处：《计算机应用研究》2011年第3期905-908,共4页Application Research of Computers

基　　金：河南省自然科学基金资助项目(102400420170);河南省教育厅青年骨干教师计划资助项目(2009GGJS-075);河南省教育厅自然基础研究计划资助项目(2010A520034)

摘　　要：介绍了蚁群优化算法利用粗搜索及精搜索过程获得多维有约束函数优化的基本思想,分析了影响蚁群优化多维有约束函数问题的关键参数,给出了获得较好的蚁群函数优化性能在优化过程中动态地自适应地调整蚁群优化算法的关键参数及α、β和ρ的指导性结论,且调整规则是α与β的值由大到小地调整,而ρ的值将由小到大地调整。建立了α、β及ρ的模糊动态调整器,给出了三个模糊控制器的参数调整过程、控制器的执行策略及控制过程。采用起重机主梁优化实例对比验证了蚁群优化算法及蚁群参数自适应调整的优化算法。结果表明,采用蚁群参数自适应调整的优化算法具有求解精度高、优化效率高及参与优化的蚁群数量少等优点。该方法是求解复杂多峰函数优化的一种极好的优化方法。This paper first introduced how to solve multi-dimensional and constrained functions by rough searching and subtle searching in ant colony optimization（ACO）,and then analyzed the key parameters which affect the optimization performance of ACO.A conclusion was drawn which required that the parameter α and β should be adjusted in descending order and ρ in ascending order.Built the fuzzy dynamic regulators of α,β and ρ next.Also gave the parameter adjustment procedure,the executed strategy and the control procedure for three fuzzy controllers.Finally,verified the performance of the proposed ACO with self-adaptive parameter adjusting in the case of a crane girder＇s optimization.Comparison results show that the self-adaptive adjusting ACO has many advantages such as the high precision solution,high optimization efficiency and less number of ant colonies over conventional ACO.This method is a promising optimization algorithm for solving complex optimization problems.

关 键 词：蚁群优化算法 模糊控制器 信息素因子分析 自适应参数调整 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]