دوره 9، شماره 3 - ( 3-1398 )
جلد 9 شماره 3 صفحات 523-499 |
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Shahrouzi M, Barzigar A, Rezazadeh D. STATIC AND DYNAMIC OPPOSITION-BASED LEARNING FOR COLLIDING BODIES OPTIMIZATION. IJOCE 2019; 9 (3) :499-523
URL: http://ijoce.iust.ac.ir/article-1-403-fa.html
URL: http://ijoce.iust.ac.ir/article-1-403-fa.html
STATIC AND DYNAMIC OPPOSITION-BASED LEARNING FOR COLLIDING BODIES OPTIMIZATION. عنوان نشریه. 1398; 9 (3) :499-523
چکیده: (20173 مشاهده)
Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimization as a powerful meta-heuristic with several engineering applications. Special combination of static and dynamic opposition-based operators are hybridized with CBO so that its performance is enhanced. The proposed OCBO is validated in a variety of benchmark test functions in addition to structural optimization and optimal clustering. According to the results, the proposed method of opposition-based learning has been quite effective in performance enhancement of parameter-less colliding bodies optimization.
نوع مطالعه: پژوهشي |
موضوع مقاله:
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دریافت: 1397/12/3 | پذیرش: 1397/12/3 | انتشار: 1397/12/3
دریافت: 1397/12/3 | پذیرش: 1397/12/3 | انتشار: 1397/12/3
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