Graph products for configuration processing of space structures

In this paper, a set theoretical framework is proposed for the population-based metaheuristic algorithms Using the proposed framework, two new set theoretical variants of the teaching–learning-based optimization (TLBO) algorithm are developed These algorithms are named as OST-TLBO and STMP-TLBO, which are acronyms for the “ordered set theoretical teaching–learning-based optimization” and “set theoretical multi-phase teaching–learning-based optimization”, respectively The present framework can be applied to other population-based metaheuristic algorithms In order to verify the stability and robustness of the presented algorithms, some optimization problems are examined These problems include four truss optimization problems with multiple natural frequency constraints Comparing the results obtained from the proposed algorithms with those of the standard version of the TLBO algorithm shows that the proposed set theoretical framework improves the performance of the standard TLBO in terms of robustness and convergence characteristics

Set theoretical variants of the teaching–learning-based optimization algorithm for optimal design of truss structures with multiple frequency constraints

On foundations of the Hardy Cross method

Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures

A. Behravesh

Papers

A set theoretical approach for configuration processing

Iterative solution of large structures

A finite difference scheme with variable rectilinear mesh for solving multi-harmonic partial differential equations

The set algebra and structural analysis