Swarm optimisation algorithms applied to large balanced communication networks

Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez‐Pérez, Juan M.; Gómez–Pulido, Juan A.; Vega‐Rodríguez, Miguel A.

doi:10.1016/j.jnca.2012.04.005

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Abc Algorithm For Finding Fsp1

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Cited by 9 publications

(2 citation statements)

References 42 publications

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“…A large number of heuristic algorithms have been proposed in the literature to solve combinatorial optimization problems (Bernardino et al, 2013;Ma et al, 2014). Some of these algorithms are designed to solve the SP problems with different types of fuzzy arc weights.…”

Section: Abc Algorithm For Finding Fspmentioning

confidence: 99%

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

2016

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Section: Abc Algorithm For Finding Fspmentioning

confidence: 99%

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

2016

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“…These colonies are characterised by the division of labour, where specific bees perform specific tasks [54,55].…”

Section: Qbeamentioning

confidence: 99%

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Bernardino¹,

Bernardino²,

Sánchez‐Pérez³

et al. 2012

IJCIS

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In the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem -the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times.

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An adaptive stochastic resonance detection method with a knowledge-based improved artificial fish swarm algorithm

Hao

Huang

Shen

et al. 2022

Multimed Tools Appl

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Swarm optimisation algorithms applied to large balanced communication networks

Cited by 9 publications

References 42 publications

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

An adaptive stochastic resonance detection method with a knowledge-based improved artificial fish swarm algorithm

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