- Title
- Discrete state transition algorithm for unconstrained integer optimization problems
- Creator
- Zhou, Xiaojun; Gao, David; Yang, Chunhua; Gui, Weihua
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/100331
- Identifier
- vital:10520
- Identifier
-
https://doi.org/10.1016/j.neucom.2015.08.041
- Identifier
- ISSN:0925-2312
- Abstract
- A recently new intelligent optimization algorithm called discrete state transition algorithm is considered in this study, for solving unconstrained integer optimization problems. Firstly, some key elements for discrete state transition algorithm are summarized to guide its well development. Several intelligent operators are designed for local exploitation and global exploration. Then, a dynamic adjustment strategy "risk and restoration in probability" is proposed to capture global solutions with high probability. Finally, numerical experiments are carried out to test the performance of the proposed algorithm compared with other heuristics, and they show that the similar intelligent operators can be applied to ranging from traveling salesman problem, boolean integer programming, to discrete value selection problem, which indicates the adaptability and flexibility of the proposed intelligent elements. (C) 2015 Elsevier B.V. All rights reserved.
- Publisher
- Elsevier
- Relation
- Neurocomputing Vol. 173, no. (2016), p. 864-874
- Rights
- Copyright © 2015 Elsevier B.V. All rights reserved.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 17 Psychology and Cognitive Sciences; State transition algorithm; Integer optimization; Traveling salesman problem; Maximum cut problem; Discrete value selection
- Full Text
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