Hybrid Teaching-Learning-Based Optimization and Harmony Search for Optimum Design of Space Trusses

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Siamak  Talatahari ¹ (Department of Civil Engineering, University of Tabriz, Tabriz, Iran. Engineering Faculty, Near East University, Turkey.)
Vahid  Goodarzimehr ² (Department of Civil Engineering, University of Tabriz, Tabriz, Iran)
Nasser  Taghizadieh ³ (Department of Civil Engineering, University of Tabriz, Tabriz, Iran)

Received: 2019-04-24 Accepted : 2019-09-01 Published : 2020-06-01

Keywords: Harmony search, Teaching-learning-based optimization, Size optimization, Structural optimization, Continuous variables,

Abstract :

The Teaching-Learning-Based Optimization (TLBO) algorithm is a new meta-heuristic algorithm which recently received more attention in various fields of science. The TLBO algorithm divided into two phases: Teacher phase and student phase; In the first phase a teacher tries to teach the student to improve the class level, then in the second phase, students increase their level by interacting among themselves. But, due to the lack of additional parameter to calculate the distance between the teacher and the mean of students, it is easily trapped at the local optimum and make it unable to reach the best global for some difficult problems. Since the Harmony Search (HS) algorithm has a strong exploration and it can explore all unknown places in the search space, it is an appropriate complement to improve the optimization process. Thus, based on these algorithms, they are merged to improve TLBO disadvantages for solving the structural problems. The objective function of the problems is the total weight of whole members which depends on the strength and displacement limits. Indeed, to avoid violating the limits, the penalty function applied in the form of stress and displacement limits. To show the superiority of the new hybrid algorithm to previous well-known methods, several benchmark truss structures are presented. The results of the hybrid algorithm indicate that the new algorithm has shown good performance.

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