A heuristic particle swarm optimizer for optimization of pin connected structures
Computers and Structures
Particle swarm approach for structural design optimization
Computers and Structures
A heuristic particle swarm optimization method for truss structures with discrete variables
Computers and Structures
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
A new optimization method: Big Bang-Big Crunch
Advances in Engineering Software
Comparison of non-deterministic search techniques in the optimum design of real size steel frames
Computers and Structures
Discrete optimum design of truss structures using artificial bee colony algorithm
Structural and Multidisciplinary Optimization
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
Information Sciences: an International Journal
Improved harmony search algorithms for sizing optimization of truss structures
Computers and Structures
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Meta-heuristic search methods have been extensively used for optimization of truss structures over the past two decades. In this study, a new meta-heuristic search method called 'teaching-learning-based optimization' (TLBO) is applied for optimization of truss structures. The method makes use of the analogy between the learning process of learners and searching for designs to optimization problems. The TLBO consists of two phases: teacher phase and learner phase. 'Teacher phase' means learning from the teacher and 'learner phase' means learning by the interaction between learners. The validity of the method is demonstrated by the four design examples. Results obtained for the design examples revealed that although the TLBO developed slightly heavier designs than the other meta-heuristic methods in a few cases, it obtained results as good as or better than the other meta-heuristic optimization methods in terms of both the optimum solutions and the convergence capability in most cases.