The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem

Joint Authors

Zhang, Tao
Chen, Zhong
Liu, June
Li, Xiong

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2017-09-14

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Biology

Abstract EN

A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP).

The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced set.

In order to illustrate the proposed method, seven numerical examples are tested and compared with results in the classical literature.

Finally, a practical problem is solved by the proposed algorithm.

American Psychological Association (APA)

Zhang, Tao& Chen, Zhong& Liu, June& Li, Xiong. 2017. The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1139844

Modern Language Association (MLA)

Zhang, Tao…[et al.]. The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1139844

American Medical Association (AMA)

Zhang, Tao& Chen, Zhong& Liu, June& Li, Xiong. The Artificial Neural Networks Based on Scalarization Method for a Class of Bilevel Biobjective Programming Problem. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1139844

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1139844