Ant Colony optimization (ACO) is a new-style simulating evolution algorithm. The behavior of real ant colonies foraging for food is simulated and used for solving optimization problems.
蚁群算法是一种新型的模拟进化算法，它通过模拟蚁群在觅食过程中寻找最短路径的方法来求解优化问题。
A novel ant colony optimization algorithm with random perturbation behavior (RPACO) based on combination of general ant colony optimization and stochastic mechanism is developed in this paper.
从随机优化技术出发，针对基本蚁群算法，提出了一种随机摄动蚁群优化算法。
Two-dimensional stock cutting problem can be settled by solving two one-dimensional knapsack problems, this paper presents a new algorithm based on the ant colony optimization idea.
基于一维问题的蚂蚁算法，本文将二维矩形件排样问题转化为一维背包问题，然后进行求解。
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