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Extreme Learning Surrogate Models in Multi-objective Optimization based on Decomposition
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AI
DOI：10.1016/j.neucom.2015.09.111.png)
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Abstract
En 中文
This paper proposes ELMOEA/D, a surrogate-assisted MOEA, for solving costly multi-objective problems in small evaluation budgets. The proposed approach encompasses a state-of-the-art MOEA based on decomposition and Differential Evolution (MOEA/D-DE) assisted by Extreme Learning Machines (ELMS). ELMOEA/D is tested in instances from three well-known benchmarks (ZDT, DTLZ and WFG) with 5-60 decision variables, 2 and 5 objectives. The ELMOEA/D's performance is also analyzed on a real problem (Airfoil Shape Optimization). The impact of some ELMs parameters and an automatic model selection mechanism is investigated. The results obtained by ELMOEA/D are compared with those of two state-of-the-art surrogate approaches (MOEA/D-RBF and ParEGO) and a non-surrogate-based MOEA (MOEA/D). The ELMOEA/D variants are among the best results for most benchmark instances and for the real problem. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Evolutionary algorithms
Multi-objective optimization
Surrogate
Extreme learning machines
MOEA/D
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W