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Behavior of crossover operators in NSGA-III for large-scale optimization problems
School of Mathematics and Big Data, Foshan University, Foshan, China; School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
School of Mathematics and Big Data, Foshan University, Foshan, China.
Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
Department of Computer Science and Technology, Ocean University of China, Qingdao, China.
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2018 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291Article in journal (Refereed) Epub ahead of print
Abstract [en]

Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

Place, publisher, year, edition, pages
Elsevier, 2018.
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-71519DOI: 10.1016/j.ins.2018.10.005Scopus ID: 2-s2.0-85055637462OAI: oai:DiVA.org:ltu-71519DiVA, id: diva2:1261941
Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2018-11-22

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Vasilakos, Athanasios

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