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Structural Engineering and Mechanics Volume 62, Number 5, June10 2017 , pages 537-550 DOI: https://doi.org/10.12989/sem.2017.62.5.537 |
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Observer-Teacher-Learner-Based Optimization: An enhanced meta-heuristic for structural sizing design |
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Mohsen Shahrouzi, Mahdi Aghabaglou and Fataneh Rafiee
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| Abstract | ||
| Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems. | ||
| Key Words | ||
| discrete optimization; constrained structural sizing, hybrid evolutionary computing | ||
| Address | ||
| Department of Engineering, Kharazmi University, 43 Shahid-Mofatteh, Tehran, Iran | ||