蚁群算法求解连续空间优化问题  

Ant colony algorithm for continuous space optimization

作　　者：杨勇[1] 宋晓峰[1] 王建飞[1] 胡上序[1] 

机构地区：[1]浙江大学智能信息工程研究所,浙江杭州310027

出　　处：《控制与决策》2003年第5期573-576,共4页Control and Decision

摘　　要：借鉴蚁群算法的进化思想,提出一种求解连续空间优化问题的蚁群算法。该算法主要包括全局搜索、局部搜索和信息素强度更新规则。在全局搜索过程中,利用信息素强度和启发式函数确定蚂蚁移动方向。在局部搜索过程中,嵌入了确定性搜索,以改善寻优性能,加快收敛速率。通过一个实例问题的求解表明了该算法的有效性。Based on ant colony evolutionary algorithm, a colony algorithm is extended for searching continuous space optimization. The new algorithm is composed of global searching, local searching and pheromone updating rule. Using pheromone and heuristic function, an ant moving direction can be determined during global searching. A deterministic searching algorithm is embedded to improve the optimization performance and enhance the fast convergence during local search. A typical example indicates the better performance of the proposed algorithm.

关 键 词：蚁群算法 连续空间优化 确定性搜索 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]