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Reasons of ACO's Success in TSP

机译:ACO在TSP中取得成功的原因

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摘要

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has empirically shown its effectiveness in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). Still, very little theory is available to explain the reasons underlying ACO's success. An ACO alternative called Omicron ACO (OA), first designed as an analytical tool, is presented. This OA is used to explain the reasons of elitist ACO's success in the TSP, given a globally convex structure of its solution space.
机译:蚂蚁殖民地优化(ACO)是由蚁群的觅食行为启发的蚁群训练,这些行为在经验上证明其效率的效果,这些问题是旅行推销员问题(TSP)等硬组合优化问题。仍然非常少的理论可以解释Aco成功的原因。展示了一个ACO替代品,名为OMICRON ACO(OA),首先被设计为分析工具。考虑到其解决方案空间的全球凸面结构,此OA用于解释Elitist ACO在TSP中成功的原因。

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