A Review on Traditional and Modern Structural Optimization: Problems and Techniques

TLDR: A classification of optimization problems and techniques is presented in this paper, where some of the most popular metaheuristic techniques utilize genetic algorithms, simulated annealing, tabu search, ant colony optimization, particle swarm optimization, harmony search, Big Bang-Big Crunch, firefly algorithm, cuckoo search, and other algorithms, and their applications in structural optimization have been investigated.

Abstract: Optimization may be used in many engineering disciplines, and structural engineering is one among them. Structural optimization deals with topology geometry and size optimization of different kinds of structures such as frames, trusses, plates, and shells to achieve minimum cost, weight, or other specific goals. A basic understanding of optimization problem specifications and the capabilities and incapabilities of solution techniques is vital for researchers in this field. There are many different kinds of structural optimization problems and solution techniques. Structural optimization problems are mostly nonlinear because of their objective(s) and constraint(s). They usually have many local minimums, which make them complex and difficult to solve using classical methods. In this chapter, a classification of optimization problems and techniques is presented. Some of the major advances during the history of structural optimization are presented here. Metaheuristic techniques have proven to be versatile and robust techniques. Some of the most popular metaheuristic techniques utilize genetic algorithms, simulated annealing, tabu search, ant colony optimization, particle swarm optimization, harmony search, Big Bang–Big Crunch, firefly algorithm, cuckoo search, and other algorithms, and their applications in structural optimization have been be investigated.