Ant colony algorithm is a new kind of simulated evolution algorithm which is put forward in near decade years. It seeks the optimal answer from the colony evolution process which includes all possible answers.
蚁群算法是近十几年才提出来的一种新型模拟进化算法，通过候选解组成的群体的进化过程来寻求最优解。
参考来源 - 基于蚁群算法的排课问题的研究But the optimization method by simulated evolution is uniquely suited to solve these high nonlinearity problems, discrimination problems and uncertainty problems. In this paper, Ant Colony Optimization presented recently was applied to Unit Commitment (UC) optimization.
而模拟进化优化方法最适于解决那些传统优化方法难以求解的高度非线性、离散性问题及组合优化问题。
参考来源 - 蚁群算法及其在电力系统机组优化组合中的应用研究·2,447,543篇论文数据，部分数据来源于NoteExpress
The ant colony optimization algorithm is a novel simulated evolution algorithm featuring a robust global searching ability.
蚁群算法是一种模拟进化算法，具有很强的全局搜索能力。
Simulated evolution and simulated annealing are two stochastic search algorithms for solving the global optimization problems. They have been widely used in different engineering areas.
模拟进化和模拟退火是解决全局优化问题的随机搜索技术，它们在工程领域有着广泛的应用。
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。
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