泛区间搜索的连续函数优化鲁棒蚁群算法  

Extensive-Domain-Search Robust Ant Colony Algorithm for Continuous Function Optimization

作　　者：陈志明[1] 陈志祥[1] 

机构地区：[1]中山大学管理学院,广州510275

出　　处：《模式识别与人工智能》2014年第6期487-495,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.70972079)资助

摘　　要：针对现有连续函数优化蚁群算法对自变量的初始区间存在敏感度问题,提出泛区间搜索的理念.通过在网格策略上加入新元素——自调整定义域的机制、自适应的蚁群规模、自适应的信息素增加强度和自适应的网格划分份数,提出泛区间搜索的连续函数优化蚁群算法.该算法可根据现有区间判断最优解的方位,实现全实数范围内的广度搜索.仿真实验表明该算法具备鲁棒性,在初始区间不含最优解的条件下也能找到最优解,且收敛速度和计算准确性受区间变化的影响较小.A concept of extensive-domain search is proposed to make ant colony optimization for continuous function overcome the sensitivity to initial domains of independent variables. By adding new elements to the gridding method, such as self-adaptive domain adjustment, self-adaptive ant size, self-adaptive pheromone increment and self-adaptive domain division, extensive.domain-search ant colony optimization （EDS-ACO） is put forward. Thus, the optimal solution can be found by EDS-ACO through an extensive search in the whole range of real numbers. Experiments show that EDS-ACO has the robustness since it can obtain the correct results in the case of initial domains without the optimal solution. The variation of initial domains has a small influence on convergence speed and computational accuracy of EDS-ACO.

关 键 词：泛区间搜索 蚁群算法 连续优化 鲁棒性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]