Volume 9, Issue 3 (6-2019)                   2019, 9(3): 499-523 | Back to browse issues page

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Abstract:   (1282 Views)
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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Type of Study: Research | Subject: Applications
Received: 2019/02/22 | Accepted: 2019/02/22 | Published: 2019/02/22