Solving biobjective set covering problem using binary cat swarm optimization algorithm

BRODERICK CRAWFORD LABRIN, RICARDO JAVIER SOTO DE GIORGIS, Hugo Caballero, Eduardo Olguín, Sanjay Misra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The set cover problem is a classical question in combinatorics, computer science and complexity theory. It is one of Karp’s 21 NP-complete problems shown to be NP-complete in 1972. Several algorithms have been proposed to solve this problem, based on genetic algorithms (GA), Particle Swarm Optimizer (PSO) and in recent years algorithms based in behavior algorithms based groups or herds of animals, such as frogs, bats, bees and domestic cats. This work presents the basic features of the algorithm based on the behavior of domestic cats and results to solve the SCP bi-objective, experimental results and opportunities to improve results using adaptive techniques applied to Cat Swarm Optimization. For this purpose we will use instances of SCP OR-Library of Beasley by adding an extra function fitness to transform the Beasly instance to Bi-Objective problem.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings
EditorsBernady O. Apduhan, Beniamino Murgante, Sanjay Misra, David Taniar, Carmelo M. Torre, Ana Maria A.C. Rocha, Shangguang Wang, Osvaldo Gervasi, Elena Stankova
PublisherSpringer Verlag
Pages220-231
Number of pages12
ISBN (Print)9783319420844
DOIs
StatePublished - 1 Jan 2016
Event16th International Conference on Computational Science and Its Applications, ICCSA 2016 - Beijing, China
Duration: 4 Jul 20167 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9786
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Computational Science and Its Applications, ICCSA 2016
CountryChina
CityBeijing
Period4/07/167/07/16

Keywords

  • Cat swarm optimization
  • Evolutionary algorithm
  • Multiobjective cat swarm optimization
  • Multiobjective problems
  • Pareto dominance
  • Swarm optimization

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