دوره 9، شماره 3 - ( 3-1398 )                   جلد 9 شماره 3 صفحات 499-523 | برگشت به فهرست نسخه ها

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چکیده:   (5642 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.
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نوع مطالعه: پژوهشي | موضوع مقاله: Applications
دریافت: ۱۳۹۷/۱۲/۳ | پذیرش: ۱۳۹۷/۱۲/۳ | انتشار: ۱۳۹۷/۱۲/۳