Modified sub-population teaching-learning-based optimization for design of truss structures with natural frequency constraints

ABSTRACT

In this paper, a modified sub-population teaching-learning-based optimization (MS-TLBO) algorithm is proposed to improve the exploration and exploitation capacities by including the concept of number of teachers, adaptive teaching factor, learning through tutorial, and self-motivated learning in the basic TLBO algorithm. The multiple frequency responses to the structural optimization problems are challenging due to its search space, which is implicit, nonconvex, nonlinear, and often leading to divergence. The viability and efficiency of the proposed method are tested by five structural benchmark problems of shape and size optimization with multiple natural frequency constraints on the planar and space trusses. The results reveal that MS-TLBO is more effective as compared to the original TLBO and other state-of-the-art algorithms.

KEYWORDS:

• Frequency constraints
• modified sub-population TLBO (MS-TLBO)
• shape and size optimization
• structural optimization
• teaching-learning-based optimization (TLBO)