Pareto Front Approximation by Ant Colony Optimization

Jaroslav Janáček, Marek Kvet

2024

Abstract

The Pareto frontier of multi-objective problem solutions denotes the unique exact solution to a problem with two or more equivalent objectives. Even when the number of problem solutions is finite, determining the precise Pareto frontier is a difficult task. Different metaheuristics can therefore provide a user with a decent approximation of the Pareto frontier in a reasonable amount of time, whereas the exact computational time-intensive approaches cannot. The acceptable computational time of metaheuristics counterbalances a solution’s deviation from the Pareto frontier. This contribution describes one of a spectrum of metaheuristics implemented with the objective of locating non-dominated solutions to the public service system design problem involving two competing criteria. The metaheuristic minimizes the difference between the present set of non-dominated solutions and the Pareto front by applying the ant colony optimization principle. A series of numerical experiments with benchmarks for which the exact Pareto frontiers are known are used to evaluate the efficacy of the proposed metaheuristic. Even though the proposed method is applicable anywhere, the used dataset comes from an Emergency Medical Service system in Slovakia, which belongs to the generally known wide class of public service systems.

Paper Citation

in Harvard Style

Janáček J. and Kvet M. (2024). Pareto Front Approximation by Ant Colony Optimization. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 200-206. DOI: 10.5220/0012262900003639

in Bibtex Style

@conference{icores24,
author={Jaroslav Janáček and Marek Kvet},
title={Pareto Front Approximation by Ant Colony Optimization},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={200-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012262900003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - Pareto Front Approximation by Ant Colony Optimization
SN - 978-989-758-681-1
AU - Janáček J.
AU - Kvet M.
PY - 2024
SP - 200
EP - 206
DO - 10.5220/0012262900003639
PB - SciTePress