A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems
Abstract Existing multi-objective evolutionary algorithms (MOEAs) have demonstrated excellent efficiency when tackling multi-objective tasks. However, its use in computationally expensive multi-objective issues is hindered by the large number of reliable evaluations needed to find Pareto-optimal sol...
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Springer
2025-01-01
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Series: | Complex & Intelligent Systems |
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Online Access: | https://doi.org/10.1007/s40747-024-01715-6 |
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author | Zijian Jiang Chaoli Sun Xiaotong Liu Hui Shi Sisi Wang |
author_facet | Zijian Jiang Chaoli Sun Xiaotong Liu Hui Shi Sisi Wang |
author_sort | Zijian Jiang |
collection | DOAJ |
description | Abstract Existing multi-objective evolutionary algorithms (MOEAs) have demonstrated excellent efficiency when tackling multi-objective tasks. However, its use in computationally expensive multi-objective issues is hindered by the large number of reliable evaluations needed to find Pareto-optimal solutions. This paper employs the semi-supervised learning technique in model training to aid in evolutionary algorithms for addressing expensive multi-objective issues, resulting in the semi-supervised learning technique assisted multi-objective evolutionary algorithm (SLTA-MOEA). In SLTA-MOEA, the value of every objective function is determined as a weighted mean of values approximated by all surrogate models for that objective function, with the weights optimized through a convex combination problem. Furthermore, the number of unlabelled solutions participating in model training is adaptively determined based on the objective evaluations conducted. A group of tests on DTLZ test problems with 3, 5, and 10 objective functions, combined with a practical application, are conducted to assess the effectiveness of our proposed method. Comparative experimental results versus six state-of-the-art evolutionary algorithms for expensive problems show high efficiency of SLTA-MOEA, particularly for problems with irregular Pareto fronts. |
format | Article |
id | doaj-art-c618c4a505f145129b36fbdd641f7cfe |
institution | Kabale University |
issn | 2199-4536 2198-6053 |
language | English |
publishDate | 2025-01-01 |
publisher | Springer |
record_format | Article |
series | Complex & Intelligent Systems |
spelling | doaj-art-c618c4a505f145129b36fbdd641f7cfe2025-02-09T13:01:19ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111211110.1007/s40747-024-01715-6A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problemsZijian Jiang0Chaoli Sun1Xiaotong Liu2Hui Shi3Sisi Wang4School of Computer Science and Technology, Taiyuan University of Science and TechnologySchool of Computer Science and Technology, Taiyuan University of Science and TechnologySchool of Electronic Information Engineering, Taiyuan University of Science and TechnologySchool of Electronic Information Engineering, Taiyuan University of Science and TechnologyShanxi Gangke Carbon Material Co., Ltd.Abstract Existing multi-objective evolutionary algorithms (MOEAs) have demonstrated excellent efficiency when tackling multi-objective tasks. However, its use in computationally expensive multi-objective issues is hindered by the large number of reliable evaluations needed to find Pareto-optimal solutions. This paper employs the semi-supervised learning technique in model training to aid in evolutionary algorithms for addressing expensive multi-objective issues, resulting in the semi-supervised learning technique assisted multi-objective evolutionary algorithm (SLTA-MOEA). In SLTA-MOEA, the value of every objective function is determined as a weighted mean of values approximated by all surrogate models for that objective function, with the weights optimized through a convex combination problem. Furthermore, the number of unlabelled solutions participating in model training is adaptively determined based on the objective evaluations conducted. A group of tests on DTLZ test problems with 3, 5, and 10 objective functions, combined with a practical application, are conducted to assess the effectiveness of our proposed method. Comparative experimental results versus six state-of-the-art evolutionary algorithms for expensive problems show high efficiency of SLTA-MOEA, particularly for problems with irregular Pareto fronts.https://doi.org/10.1007/s40747-024-01715-6Expensive multi-objective evolutionary algorithmSemi-supervised learningGaussian process model |
spellingShingle | Zijian Jiang Chaoli Sun Xiaotong Liu Hui Shi Sisi Wang A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems Complex & Intelligent Systems Expensive multi-objective evolutionary algorithm Semi-supervised learning Gaussian process model |
title | A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems |
title_full | A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems |
title_fullStr | A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems |
title_full_unstemmed | A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems |
title_short | A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems |
title_sort | semi supervised learning technique assisted multi objective evolutionary algorithm for computationally expensive problems |
topic | Expensive multi-objective evolutionary algorithm Semi-supervised learning Gaussian process model |
url | https://doi.org/10.1007/s40747-024-01715-6 |
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