Overview on Binary Optimization Using Swarm-Inspired Algorithms

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature in...

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Autores principales:	Mariana Macedo, Hugo Siqueira, Elliackin Figueiredo, Clodomir Santana, Rodrigo C. Lira, Anu Gokhale, Carmelo Bastos-Filho
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Binary optimisation swarm intelligence metaheuristics fitness function Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/365a682cc7af4cd9beca262e368d1f09
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id	oai:doaj.org-article:365a682cc7af4cd9beca262e368d1f09
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spelling	oai:doaj.org-article:365a682cc7af4cd9beca262e368d1f092021-11-18T00:01:31ZOverview on Binary Optimization Using Swarm-Inspired Algorithms2169-353610.1109/ACCESS.2021.3124710https://doaj.org/article/365a682cc7af4cd9beca262e368d1f092021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9597543/https://doaj.org/toc/2169-3536Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarm-based techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach - binary decision variables and binary search space - is more efficient for binary optimisation in accuracy and computational cost.Mariana MacedoHugo SiqueiraElliackin FigueiredoClodomir SantanaRodrigo C. LiraAnu GokhaleCarmelo Bastos-FilhoIEEEarticleBinary optimisationswarm intelligencemetaheuristicsfitness functionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149814-149858 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Binary optimisation swarm intelligence metaheuristics fitness function Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Binary optimisation swarm intelligence metaheuristics fitness function Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mariana Macedo Hugo Siqueira Elliackin Figueiredo Clodomir Santana Rodrigo C. Lira Anu Gokhale Carmelo Bastos-Filho Overview on Binary Optimization Using Swarm-Inspired Algorithms
description	Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarm-based techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach - binary decision variables and binary search space - is more efficient for binary optimisation in accuracy and computational cost.
format	article
author	Mariana Macedo Hugo Siqueira Elliackin Figueiredo Clodomir Santana Rodrigo C. Lira Anu Gokhale Carmelo Bastos-Filho
author_facet	Mariana Macedo Hugo Siqueira Elliackin Figueiredo Clodomir Santana Rodrigo C. Lira Anu Gokhale Carmelo Bastos-Filho
author_sort	Mariana Macedo
title	Overview on Binary Optimization Using Swarm-Inspired Algorithms
title_short	Overview on Binary Optimization Using Swarm-Inspired Algorithms
title_full	Overview on Binary Optimization Using Swarm-Inspired Algorithms
title_fullStr	Overview on Binary Optimization Using Swarm-Inspired Algorithms
title_full_unstemmed	Overview on Binary Optimization Using Swarm-Inspired Algorithms
title_sort	overview on binary optimization using swarm-inspired algorithms
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/365a682cc7af4cd9beca262e368d1f09
work_keys_str_mv	AT marianamacedo overviewonbinaryoptimizationusingswarminspiredalgorithms AT hugosiqueira overviewonbinaryoptimizationusingswarminspiredalgorithms AT elliackinfigueiredo overviewonbinaryoptimizationusingswarminspiredalgorithms AT clodomirsantana overviewonbinaryoptimizationusingswarminspiredalgorithms AT rodrigoclira overviewonbinaryoptimizationusingswarminspiredalgorithms AT anugokhale overviewonbinaryoptimizationusingswarminspiredalgorithms AT carmelobastosfilho overviewonbinaryoptimizationusingswarminspiredalgorithms
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Overview on Binary Optimization Using Swarm-Inspired Algorithms

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