Volume 9, Issue 3 (6-2019)
IJOCE 2019, 9(3): 499-523 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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-en.html
URL: http://ijoce.iust.ac.ir/article-1-403-en.html
Abstract: (21085 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.
Keywords: Opposition-based learning, truss structure, building frame, sizing design, geometry optimization, ground motion clustering.
Type of Study: Research |
Subject:
Applications
Received: 2019/02/22 | Accepted: 2019/02/22 | Published: 2019/02/22
Received: 2019/02/22 | Accepted: 2019/02/22 | Published: 2019/02/22
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
