Abstract
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.
Highlights
The Set Covering Problem (SCP) is a typical optimization problem that serves as a model for many applications in the real world
It is a Combinatorial Optimization Problem (COP) which can be formulated as an Integer Linear Programming (ILP) where the goal is to minimize the number of sets such that every element of the set from the universe will be covered and every set is either in the set cover or not
Ant Colony Optimization Metaheuristic was applied to the Set Covering Problem
Summary
The Set Covering Problem (SCP) is a typical optimization problem that serves as a model for many applications in the real world. They conducted a research to solve SCP based from the recently developed, populationbased metaheuristic named Ant Colony Optimization (ACO) algorithm. Given a set of facilities and the areas covered by the facility, a number of ants determine what facility to choose using a probability function for ACO which is based on pheromone trails and heuristic information, while the area it chooses is randomized. After the chosen areas by the ant satisfies the system constraints or covers all the areas in the system, a ant will proceed, and the pheromone trails and heuristic information will be updated This will continue until all ants have passed, and the best solution for the problem is returned and recorded. This paper is concerned in determining the optimal placement of Wi-Fi Access Points in the University of the Philippines Los Baños (UPLB) Math Building using ACO metaheuristic
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
