Abstract: A scaleable chaotic search algorithm is embedded into a modified version of a special ant colony optimization algorithm called touring ant colony optimization (TACO) to form a new algorithm named Scaleable Chaotic Ant Colony Optimization (SCACO). The embedded chaotic search algorithm is used to find a better solution whenever all the ants have finished a path-construct operation. The chaotic search algorithm searches the space around the best-so-far ant. And the radius of the searching area is decreased as the ant colony algorithm goes on. The scaleable search technology helps the ant colony algorithm to avoid dropping into local optima. And it also helps improving the accuracy of the solution generated by SCACO. A set of benchmark functions is used to test SCACO. And the experiment result shows that SCACO is good at solving function optimization problems including cheating functions and high-dimensional functions.

Key words: Ant Colony Algorithm, Chaos, scaleable, function optimization

摘要： 本文将变尺度混沌搜索算法融合到蚁群算法中，并用于求解连续空间优化问题。蚁群算法每一次迭代结束时，就使用混沌搜索算子在当前全局最优解附近搜索更好的解。而随着蚁群算法的进行，混沌算子搜索范围逐渐缩小，这样，混沌算子在蚁群搜索的初期起到防止陷入局部最优的作用，在蚁群搜索后期起到提高搜索精度的作用。将变尺度混沌蚁群优化算法用于求解函数优化问题的实验结果表明，该算法在求解包括欺骗性函数和高维函数在内的多种测试函数优化问题方面具有很好的效果。

关键词: 蚁群算法, 混沌, 变尺度, 函数优化