Incorporating Hybrid Operators on an Immune Based Framework for Multiobjective Optimization

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dc.contributor.authorDESTRO, R. D. C.
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2022-01-12T21:59:15Z
dc.date.available2022-01-12T21:59:15Z
dc.date.issued2015-01-12
dc.description.abstract© 2015 IEEE.This paper presents an Artificial Immune System framework for solving Multiobjective Optimization Problems that makes use of two immunologic operators with non-immunologic features, called Hybrid Operators, which were inspired from Artificial Neural Networks and Genetic Algorithms techniques. The first of these operators makes use of the concept of Momentum to improve the performance of mutation while the second incorporates the recombination operator. The new AIS framework is called MOHAIS, for Multiobjective Optimization Hybrid Artificial Immune System. The proposed framework was used to implement one traditional AIS algorithm and a new hybrid AIS, based on proposed hybrid operators. Experiments were made to evaluate hybrid operators performance, to compare the hybrid AIS with one traditional AIS and three Multiobjective Optimization Evolutionary Algorithms, in nine traditional benchmark problems. Results showed that hybrid operators improved the performance of the AIS with satisfactory results in all nine scenarios.
dc.description.firstpage2809
dc.description.lastpage2816
dc.identifier.citationDESTRO, R. D. C.; BIANCHI, R. Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Jan. 2015.
dc.identifier.doi10.1109/SMC.2015.490
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3917
dc.relation.ispartofProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
dc.rightsAcesso Restrito
dc.titleIncorporating Hybrid Operators on an Immune Based Framework for Multiobjective Optimization
dc.typeArtigo de evento
fei.scopus.citations4
fei.scopus.eid2-s2.0-84964526660
fei.scopus.subjectArtificial Immune System
fei.scopus.subjectBench-mark problems
fei.scopus.subjectMulti-objective optimization evolutionary algorithms
fei.scopus.subjectMulti-objective optimization problem
fei.scopus.subjectNeural networks and genetic algorithms
fei.scopus.subjectRecombination operators
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964526660&origin=inward
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