Montgomery James, Randall Marcus, Hendtlass Tim
Faculty of Information Technology, Bond University, QLD 4229, Australia.
Artif Life. 2005 Summer;11(3):269-91. doi: 10.1162/1064546054407149.
Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.
蚁群优化算法（ACO）是一种构造性元启发式算法，它利用蚁群轨迹信息素的类似物来了解解决方案的优良特征。关键在于，针对特定问题的信息素表示通常是凭直觉选择的，而不是通过任何系统的过程来确定。在某些表示中，不同的解决方案会多次出现，这增加了搜索空间的有效大小，并可能误导蚂蚁对这些解决方案的真正学习价值的判断。在本文中，我们基于问题模型的特征提出了一种新颖的系统，用于自动生成合适的信息素表示，以确保解决方案的信息素表示是唯一的。这是开发广义ACO系统的第一阶段，该系统几乎无需修改或只需进行少量修改就能应用于广泛的问题。然而，我们提出的系统可用于开发任何特定问题的ACO算法。
