Li Yan-jun, Wu Tie-jun
Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China.
J Zhejiang Univ Sci. 2003 Jan-Feb;4(1):40-6. doi: 10.1631/jzus.2003.0040.
Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.
蚁群算法是一类用于优化问题的新型进化计算方法，尤其适用于排序型组合优化问题。本文提出了一种自适应蚁群算法来解决连续空间优化问题，该算法采用一种基于新目标函数的启发式信息素分配方法进行信息素更新，以筛选候选解。通过根据目标值自我调整蚂蚁的路径搜索行为，可以更快地找到全局最优解。在解决两个具有多个极值的基准问题时，将该算法的性能与基本蚁群算法和平方二次规划方法进行了比较。结果表明，该算法的效率和可靠性得到了极大提高。
