Solving dynamic multi-objective optimization problems via support vector machine

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI)
PublisherIEEE
Pages819-824
ISBN (Electronic)978-1-5386-4362-4
Publication statusPublished - Mar 2018

Conference

Title10th International Conference on Advanced Computational Intelligence, ICACI 2018
PlaceChina
CityXiamen, Fujian
Period29 - 31 March 2018

Abstract

Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.

Research Area(s)

  • Dynamic Multi-objective Optimization Problems, Pareto Optimal Set, Support Vector Machine

Citation Format(s)

Solving dynamic multi-objective optimization problems via support vector machine. / JIANG, Min; HU, Weizhen; QIU, Liming; SHI, Minghui; TAN, Kay Chen.

Proceedings of 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2018. p. 819-824.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review