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Observer-Teacher-Learner-Based Optimization: An enhanced meta-heuristic for structural sizing design

  • Shahrouzi, Mohsen ;
  • Aghabaglou, Mahdi ;
  • Rafiee, Fataneh
  • Received : 2015.12.04
  • Accepted : 2017.04.11
  • Published : 2017.06.10

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.

Keywords

discrete optimization;constrained structural sizing;hybrid evolutionary computing

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