Ant colony optimisation and the traveling salesperson problem: hardness, features and parameter settings

Authors:
Samadhi Nallaperuma;Markus Wagner;Frank Neumann
Affiliations:
School of Computer Science, University of Adelaide, Adelaide, AA, Australia;School of Computer Science, University of Adelaide, Adelaide, Australia;School of Computer Science, University of Adelaide, Adelaide, Australia
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

Citing 7
Cited 0

MAX-MIN Ant system

Future Generation Computer Systems
Solution of a Min-Max Vehicle Routing Problem

INFORMS Journal on Computing
Ant Colony Optimization

Ant Colony Optimization
Understanding TSP difficulty by learning from evolved instances

LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
On MAX - MIN ant system's parameters

ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A short convergence proof for a class of ant colony optimizationalgorithms

IEEE Transactions on Evolutionary Computation
A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem

Proceedings of the twelfth workshop on Foundations of genetic algorithms XII

Quantified Score

Hi-index	0.00

Visualization

Abstract

Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance.