Abstract: A new Improved Ant Colony Algorithm（IACA） based on grid strategy is presented for continuous space optimization. IACA designs a special pheromone update strategy that the algorithm can update pheromone not using value of solution. So it reduces negative effect on performance by differentiation of objective function values, and the pheromone can directly be used to select probability during the solution construction procedure. In the test of the application to continuous space optimization functions, the proposed algorithm achieves good search ability.

Key words: Ant Colony Optimization（ACO）, continuous space optimization, grading method, pheromone

摘要： 针对连续空间函数优化问题，提出一种基于网格划分策略的改进蚁群算法。算法使用一种特殊的信息素更新策略，使得更新信息素时不需要使用解的具体目标函数值，从而降低了目标函数值差异化给算法性能带来的不利影响，并且网格点上的信息素可以直接作为构建解过程中的转移概率。对几种典型的连续函数优化问题进行了测试，实验结果表明所提出算法具有很强的搜索能力。

关键词: 蚁群优化, 连续空间优化, 网格法, 信息素