Open AccessApplication of ant colony optimization metaheuristic on set covering problems
Author(s) -
Christian Alvin H. Buhat,
Jerson Ken Villamin,
Genaro A. Cuaresma
Publication year - 2022
Publication title -
mathematics in applied sciences and engineering
Language(s) - English
Resource type - Journals
ISSN - 2563-1926
DOI - 10.5206/mase/14018
Subject(s) - metaheuristic , ant colony optimization algorithms , parallel metaheuristic , mathematical optimization , computer science , set (abstract data type) , ant colony , optimization problem , artificial intelligence , mathematics , meta optimization , programming language
Ant Colony Optimization (ACO) metaheuristic is a multi-agent system in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. Set Covering Problems (SCP), on the other hand, deal with maximizing the coverage of every subset while the weight nodes used must be minimized. In this paper, ACO was adapted and used to solve a case of Set Covering Problem. The adapted ACO for solving the SCP was implemented as a computer program using SciLab 5.4.1. The problem of determining the optimal location of Wi-Fi Access Points using the 802.11n protocol in the UP Los Banos Math Building was solved using this metaheuristic. Results show that in order to have 100% coverage of the MB, 7 access points are required. Methodology of the study can be adapted and results of the study can be used by decision makers on related optimization problems.