A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems

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. T...

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Veröffentlicht in:Complex & intelligent systems Jg. 11; H. 2; S. 128 - 11
Hauptverfasser: Jiang, Zijian, Sun, Chaoli, Liu, Xiaotong, Shi, Hui, Wang, Sisi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.02.2025
Springer Nature B.V
Springer
Schlagworte:
Algorithms
Complexity
Computational Intelligence
Data Structures and Information Theory
Engineering
Evolutionary algorithms
Expensive multi-objective evolutionary algorithm
Gaussian process model
Genetic algorithms
Machine learning
Multiple objective analysis
Original Article
Pareto optimum
Semi-supervised learning
Semi-supervised learning
Expensive multi-objective evolutionary algorithm
Gaussian process model
ISSN:2199-4536, 2198-6053
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Zusammenfassung: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.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-024-01715-6