An efficient hybrid multi-objective memetic algorithm for the frequency assignment problem
This paper investigates hybridization of multi-objective memetic algorithm and artificial immune system (AIS) for the Frequency Assignment Problem (FAP) in cellular mobile networks. The considered objectives to minimize are the total interference, the maximal interference, and the number of used fre...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 87; s. 103265 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.01.2020
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | This paper investigates hybridization of multi-objective memetic algorithm and artificial immune system (AIS) for the Frequency Assignment Problem (FAP) in cellular mobile networks. The considered objectives to minimize are the total interference, the maximal interference, and the number of used frequencies. The proposed approach integrates FAP-specific local search into the evolutionary process to overcome the shortcoming of the multi-objective genetic algorithm, as well as clonal selection and receptor editing, which aims to improve the algorithm exploration and exploitation abilities. Based on the hypervolume metric, the proposed hybrid multi-objective algorithm produces high quality solutions as proved by the tests performed over COST259 instances and corroborated by the comparisons with the most frequently referred algorithms in the related literature. Furthermore, the effect and the behaviour of the main parameters of our algorithm and the interaction between them are analysed using the Design of Experiment (DOE).
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•The proposed approach is a multi-objective memetic algorithm that integrates immune operators in its evolutionary process to improve the algorithm exploration and exploitation abilities.•As application, we deal with the Frequency Assignment Problem (FAP) in cellular networks considering three objectives to minimize: the total interference, the maximal interference, and the number of used frequencies.•The proposed approach integrates FAP-specific local search into the evolutionary process as well as clonal selection and receptor editing inherited from AIS.•The behaviour of the main algorithm factors and the interaction between them are analysed using the ANOVA statistical test.•The performances of the newly proposed algorithm are measured in terms of hypervolume on COST259 instances. |
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| AbstractList | This paper investigates hybridization of multi-objective memetic algorithm and artificial immune system (AIS) for the Frequency Assignment Problem (FAP) in cellular mobile networks. The considered objectives to minimize are the total interference, the maximal interference, and the number of used frequencies. The proposed approach integrates FAP-specific local search into the evolutionary process to overcome the shortcoming of the multi-objective genetic algorithm, as well as clonal selection and receptor editing, which aims to improve the algorithm exploration and exploitation abilities. Based on the hypervolume metric, the proposed hybrid multi-objective algorithm produces high quality solutions as proved by the tests performed over COST259 instances and corroborated by the comparisons with the most frequently referred algorithms in the related literature. Furthermore, the effect and the behaviour of the main parameters of our algorithm and the interaction between them are analysed using the Design of Experiment (DOE).
[Display omitted]
•The proposed approach is a multi-objective memetic algorithm that integrates immune operators in its evolutionary process to improve the algorithm exploration and exploitation abilities.•As application, we deal with the Frequency Assignment Problem (FAP) in cellular networks considering three objectives to minimize: the total interference, the maximal interference, and the number of used frequencies.•The proposed approach integrates FAP-specific local search into the evolutionary process as well as clonal selection and receptor editing inherited from AIS.•The behaviour of the main algorithm factors and the interaction between them are analysed using the ANOVA statistical test.•The performances of the newly proposed algorithm are measured in terms of hypervolume on COST259 instances. |
| ArticleNumber | 103265 |
| Author | Kiouche, Abd Errahmane Benbouzid-SiTayeb, Fatima Keddar, Mohamed Reda Bessedik, Malika |
| Author_xml | – sequence: 1 givenname: Abd Errahmane surname: Kiouche fullname: Kiouche, Abd Errahmane email: ca_kiouche@esi.dz – sequence: 2 givenname: Malika surname: Bessedik fullname: Bessedik, Malika email: m_bessedik@esi.dz – sequence: 3 givenname: Fatima surname: Benbouzid-SiTayeb fullname: Benbouzid-SiTayeb, Fatima email: f_sitayeb@esi.dz – sequence: 4 givenname: Mohamed Reda surname: Keddar fullname: Keddar, Mohamed Reda email: cm_kaddar@esi.dz |
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| Cites_doi | 10.1007/s00521-012-1046-7 10.1109/72.641459 10.1007/s00170-015-8052-8 10.1109/CEC.2016.7743969 10.1109/TEVC.2005.850260 10.1016/j.cosrev.2009.07.001 10.1007/s10479-007-0178-0 10.1145/1276958.1276972 10.1109/ICC.2017.7997007 10.1108/02644401211206034 10.1109/SOCPAR.2015.7492816 10.1145/1389095.1389396 10.1016/j.orl.2006.01.009 10.1023/A:1009690321348 10.1109/PROC.1980.11899 10.1109/ACCESS.2018.2882595 10.1016/j.ejor.2007.02.047 10.1023/A:1016540724870 10.1007/s00500-010-0653-4 10.1016/j.procs.2018.07.262 10.1109/4235.996017 10.1109/TEVC.2016.2641477 10.1109/ISTT.2014.7238206 10.1109/ICC.2016.7510702 10.1016/j.future.2004.03.014 10.1145/359094.359101 10.1109/72.265956 10.1007/s10791-005-6619-y 10.1007/s00500-014-1337-2 10.1109/NABIC.2009.5393695 10.1109/CEC.2011.5949744 10.1504/IJMHEUR.2014.058861 10.1109/25.69987 10.1007/978-3-319-66939-7_8 |
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| Keywords | Multi-objective genetic algorithm Memetic algorithm Local search Clonal selection Receptor editing Frequency assignment problem |
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