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Erschienen in:
2016 | OriginalPaper | Buchkapitel
An Artificial Fish Swarm Optimization Algorithm to Solve Set Covering Problem
verfasst von : Broderick Crawford, Ricardo Soto, Eduardo Olguín, Sebastián Mansilla Villablanca, Álvaro Gómez Rubio, Adrián Jaramillo, Juan Salas
Erschienen in: Trends in Applied Knowledge-Based Systems and Data Science
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Abstract
The Set Covering Problem (SCP) consists in finding a set of solutions that allow to cover a set of necessities with the minor possible cost. There are many applications of this problem such as rolling production lines or installation of certain services like hospitals. SCP has been solved before with different algorithms like genetic algorithm, cultural algorithm or firefly algorithm among others. The objective of this paper is to show the performance of an Artificial Fish Swarm Algorithm (AFSA) in order to solve SCP. This algorithm, simulates the behavior of a fish shoal inside water and it uses a population of points in space to represent the position of a fish in the shoal. Here we show a study of its simplified version of AFSA in a binary domain with its modifications applied to SCP. This method was tested on SCP benchmark instances from OR-Library website.