A Novel Multi-Objective Immune Memetic Algorithm for the Frequency Assignment Problem
| Autor: | Mohamed Reda Keddar, Abd Errahmane Kiouche, Fatima Benbouzid-Sitayeb, Malika Bessedik |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial immune system
business.industry Computer science Crossover 020206 networking & telecommunications 02 engineering and technology Mutation (genetic algorithm) 0202 electrical engineering electronic engineering information engineering Cellular network General Earth and Planetary Sciences Memetic algorithm 020201 artificial intelligence & image processing Local search (optimization) Artificial intelligence business General Environmental Science Clonal selection |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2019.09.161 |
| Popis: | This paper presents a multi-objective immune memetic algorithm to the challenge of solving the Frequency Assignment Problem (FAP) in cellular networks seeking the minimization of the network’s total interference, the maximum interference and the number of used frequencies. The originality of the proposed approach lies in integrating a FAP-specific local search into its evolutionary process instead of crossover and mutation, as well as a guided diversification strategy for better performances. Moreover, the algorithm is supplemented with a clonal selection, inherited from Artificial Immune Systems (AIS), which aims to improve the algorithm exploration and exploitation abilities. Computational experiments performed over COST259 instances show the efficiency of the newly proposed evolutionary multi-objective algorithm and corroborated by the comparisons we did with the most frequently referred algorithm in the related literature. Furthermore, the effect of the main parameters and the interaction between them is analyzed using statistical tools. |
| Databáze: | OpenAIRE |
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