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

DOI

10.1080/18756891.2012.718157How to use a DOI?

Keywords

Evolutionary Algorithms, Swarm Intelligence, Terminal Assignment Problem, Genetic algorithm with a new swarm mutation operator, Queen-bee Evolutionary Algorithm

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Eugénia Moreira Bernardino
AU  - Anabela Moreira Bernardino
AU  - Juan Manuel Sánchez-Pérez
AU  - Juan Antonio Gómez-Pulido
AU  - Miguel Ángel Vega-Rodríguez
PY  - 2012
DA  - 2012/08/01
TI  - Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 745
EP  - 761
VL  - 5
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.718157
DO  - 10.1080/18756891.2012.718157
ID  - Bernardino2012
ER  -