Abstract: When Ant Colony Algorithm（ACA） is applied to find the source in solving Inverse Heat Conduction Problem（IHCP）, it is easy to fall into local optimal solution and the convergence speed is very slow. In order to solve this problem, this paper makes use of the ergodicity and initial value sensitivity of Chaotic Algorithm（CA） to establish Chaos Ant Colony Algorithm（CACA） based on chaos path selection mechanism and local search mechanism. Calculation results show that the CACA established can solve the IHCP well and improves the calculation precision and computing speed.

Key words: Ant Colony Algorithm（ACA）, chaotic, Inverse Heat Conduction Problem（IHCP）, Chaos Ant Colony Algorithm（CACA）

摘要： 为克服蚁群算法应用于寻源导热逆问题求解时容易陷入局部最优解和收敛速度慢的不足，利用混沌算法的遍历性和对初值的敏感性，将其融入到蚁群算法中，建立了基于混沌路径选择机制和局部混沌搜索机制的混沌-蚁群算法。计算结果表明，建立的混沌-蚁群算法可以很好地解决寻源导热逆问题，较蚁群算法而言，提高了计算精度和计算速度。

关键词: 蚁群算法, 混沌, 导热逆问题, 混沌蚁群算法