Ant-behavior-inspired algorithms are finding applications in many human world problems. Following are abstracts from two excellent papers available online, discussing ant-inspired algorithms for the traditional vehicle routing and communications network routing problems.

An Improved Ant System Algorithm for the Vehicle Routing Problem
Bern Bullnheimer, Richard F. Hartl, and Christine Strauss
Institute of Management Science, University of Vienna

The Ant System is a distributed metaheuristic that combines an adaptive memory with a local heuristic function to repeatedly construct solutions of hard combinatorial optimization problems. We present in this paper an improved ant system algorithm for the Vehicle Routing Problem with one central depot and identical vehicles. Computational results on fourteen benchmark problems from the literature are reported and a comparison with five other metaheuristic approaches to solve vehicle routing problems is made.

Keywords: ant system, adaptive memory, vehicle routing, metaheuristics
Postscript File

AntNet: A Mobile Agents Approach to Adaptive Routing
Gianni Di Caro and Marco Dorigo
IRIDIA, Universite Libre de Bruxelles

This paper introduces AntNet, a new routing algorithm for communications networks. AntNet is an adaptive, distributed, mobile-agents-based algorithm which was inspired by recent work on the ant colony metaphor. We apply AntNet to a datagram network and compare it with both static and adaptive state-of-the-art routing algorithms. We ran experiments for various paradigmatic temporal and spatial traffic distributions. AntNet showed both very good performance and robustness under all the experimental conditions with respect to its competitors.

PDF File

More Et Al.