Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Kazemzadeh Azad, Saeid
• Is Part Of: Engineering optimization. Volume 50:Issue 1(2018)
• Page Start: 89
• Page End: 105
• Publication Date: 2018-01-02
• Subjects: Discrete optimization -- steel trusses -- metaheuristic algorithms -- big bang–big crunch algorithm -- AISC-LRFD; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1284833
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : In spite of considerable research work on the development of efficient algorithms for discrete sizing optimization of steel truss structures, only a few studies have addressed non-algorithmic issues affecting the general performance of algorithms. For instance, an important question is whether starting the design optimization from a feasible solution is fruitful or not. This study is an attempt to investigate the effect of seeding the initial population with feasible solutions on the general performance of metaheuristic techniques. To this end, the sensitivity of recently proposed metaheuristic algorithms to the feasibility of initial candidate designs is evaluated through practical discrete sizing of real-size steel truss structures. The numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization. This paves the way for efficient metaheuristic optimization of large-scale structural systems.