期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 29, 期 -, 页码 1-12出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2013.12.004
关键词
Opposition-based learning; Opposite point; Soft computing algorithms; Function optimization
类别
资金
- National Natural Science Foundation of China [61100173, 61272283, 61305083]
- China Postdoctoral Science Foundation [2013M530534]
Diverse forms of opposition are already existent virtually everywhere around us, and utilizing opposite numbers to accelerate an optimization method is a new idea. Since 2005, opposition-based learning is a fast growing research field in which a variety of new theoretical models and technical methods have been studied for dealing with complex and significant problems. As a result, an increasing number of works have thus proposed. This paper provides a survey on the state-of-the-art of research, reported in the specialized literature to date, related to this framework. This overview covers basic concepts, theoretical foundation, combinations with intelligent algorithms, and typical application fields. A number of challenges that can be undertaken to help move the field forward are discussed according to the current state of the opposition-based learning. (C) 2013 Elsevier Ltd. All rights reserved.
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