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Feedback neural network for constrained bi-objective convex optimization
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DOI：10.1016/j.neucom.2022.09.120.png)
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Abstract
En 中文
Bi-objective optimization problems occur widely in engineering and science. This paper considers lexico-graphic method solution of the bi-objective optimization problems, feedback neural networks are adopted as optimization tools. The two objectives are sorted according to their priority, and then the bi-objective optimization problem is converted to two optimization problems with single-objective, namely, the high-priority optimization problem and the low-priority optimization problem. First, a clas-sical neural network is applied for the high-priority optimization problem. Then, a novel feedback neural network is constructed to find an optimal solution of the low-priority optimization problem in the opti-mal solution set of the high-priority optimization problem. It is proved that any accumulation point of the state sequence of the proposed feedback neural network is a Pareto optimal solution to the considered bi-objective optimization problem. In contrast with the existing neural networks, the proposed neural net-work has simple structure and relies on weaker convergence conditions. Finally, some numerical exam-ples and an application in fuzzy optimization show the effectiveness of the proposed neural network.CO 2022 Elsevier B.V. All rights reserved.
Keywords:
Bi-objective optimization
Lexicographic method
Feedback neural network
Convergence analysis
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W