Binary Optimisation with an Urban Pigeon-Inspired Swarm Algorithm

Rojas–Galeano, Sergio

doi:10.1007/978-3-030-31019-6_17

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The work from Hichem et al overcoming traditional approaches, as BPSO, BGA and BHS. Pigeon: Rojas-Galeano proposed a modified version of the Urban Pigeon-Inspired Swarm Algorithm to deal with binary problems [158]. Urban pigeons present feeding habits that can be classified into two modes, flock feeding and solitary feeding (similar to the Cat Swarm Optimization).…”

Section: Other Algorithmsmentioning

confidence: 99%

Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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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 swarmbased 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.

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Section: Other Algorithmsmentioning

confidence: 99%

Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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An Agent-Based Model of Urban Pigeon Swarm Optimisation

Garzón

Rojas–Galeano

2019

2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

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Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

et al. 2022

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For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism’s impact on the metaheuristic algorithms’ performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances.

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Binary Optimisation with an Urban Pigeon-Inspired Swarm Algorithm

Cited by 3 publications

References 15 publications

Overview on Binary Optimization Using Swarm-Inspired Algorithms

Overview on Binary Optimization Using Swarm-Inspired Algorithms

An Agent-Based Model of Urban Pigeon Swarm Optimisation

Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

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