Adaptive dimensional search: A new metaheuristic algorithm for discrete truss sizing optimization

Hasançebi, O.; Azad, Saeid Kazemzadeh

doi:10.1016/j.compstruc.2015.03.014

Cited by 122 publications

(43 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimization results obtained by the proposed algorithm are compared with some most recent metaheuristics, including MBA [11], TLBO [5], ADS [12] and aeDE [14]. The statistical results of optimal weight are given in Tab.…”

Section: Ten-bar Trussmentioning

confidence: 99%

“…Recently developed metaheuristics have shown good performance, for example, are: harmony search algorithm (SAHS) [3], teaching-learning-based optimization (TLBO) algorithm [4,5], chaotic swarming of particle (CSP) algorithm [6], colliding bodies optimization (CBO) [7,8], flower pollination algorithm (FPA) [9], water c 2016 Vietnam Academy of Science and Technology cycle algorithm (WCA) [10] and mine blast algorithm (MBA) [10,11], adaptive dimensional search (ADS) [12], and some new variants of the well-known differential evolution including adaptive differential evolution algorithm (ADEA) [1], improved constrained differential evolution using discrete variables (D-ICDE) [13], adaptive elitist differential evolution (aeDE) [14] and reliability-based improved constrained differential evolution (SORA-ICDE) [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An evolutionary-based optimization algorithm for truss sizing design

Pham

2016

Vietnam J. Mech.

View full text Add to dashboard Cite

Abstract. In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

show abstract

Section: Ten-bar Trussmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An evolutionary-based optimization algorithm for truss sizing design

Pham

2016

Vietnam J. Mech.

View full text Add to dashboard Cite

show abstract

“…Reference [7] used the ground structure approach for truss topology optimization. Similar methods of structural optimization using the ground structure approach have been applied to diverse problems [2][3][4] using various algorithms [5,[8][9][10][11]. Furthermore, with improved optimization heuristics and higher computation capabilities, optimization has been performed for complex structures.…”

Section: Literature Reviewmentioning

confidence: 99%

Extraction of Structural System Designs from Topologies via Morphological Analysis and Artificial Intelligence

Jootoo

Lattanzi

2018

Designs

View full text Add to dashboard Cite

Structural system design is the process of giving form to a set of interconnected components subjected to loads and design constraints while navigating a complex design space. While safe designs are relatively easy to develop, optimal designs are not. Modern computational optimization approaches employ population based metaheuristic algorithms to overcome challenges with the system design optimization landscape. However, the choice of the initial population, or ground structure, can have an outsized impact on the resulting optimization. This paper presents a new method of generating such ground structures, using a combination of topology optimization (TO) and a novel system extraction algorithm. Since TO generates monolithic structures, rather than systems, its use for structural system design and optimization has been limited. In this paper, truss systems are extracted from topologies through morphological analysis and artificial intelligence techniques. This algorithm, and its assessment, constitutes the key contribution of this paper. The structural systems obtained are compared with ground truth solutions to evaluate the performance of the algorithms. The generated structures are also compared against benchmark designs from the literature. The results indicate that the presented truss generation algorithm produces structures comparable to those generated through metaheuristic optimization, while mitigating the need for assumptions about initial ground structures.

show abstract

“…However, most of the papers found in the literature regarding truss optimization using metaheuristics focused on one or two of these aspects. Indeed, metaheuristic algorithms have been extensively applied to the size optimization of trusses such as Goldberg and Samtani (1986), Adeli and Kamal (1991), and Rajeev and Krishnamoorthy (1992), Bland (2001), Lee et al (2005), Kaveh and Talatahari (2009a), Kaveh and Talatahari (2009b), Sonmez (2011), , Sadollaha et al (2015), Kaveh and Mahdavi (2014), , Degertekin (2012), Degertekin and Hayalioglu (2013), Zhanga et al (2014), Hasançebi and Kazemzadeh Azad (2014), Kazemzadeh Azad and Hasançebi (2015a), Hasançebi and Kazemzadeh Azad (2015), to name just a few.…”

Section: Introductionmentioning

confidence: 99%

A Backtracking Search Algorithm for the Simultaneous Size, Shape and Topology Optimization of Trusses

Souza

Miguel

López

et al. 2016

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

This paper presents a Backtracking Search Optimization algorithm (BSA) to simultaneously optimize the size, shape and topology of truss structures. It focuses on the optimization of these three aspects since it is well known that the most effective scheme of truss optimization is achieved when they are simultaneously considered. The minimization of structural weight is the objective function, imposing displacement, stress, local buckling and/or kinematic stability constraints. The effectiveness of the BSA at solving this type of optimization problem is demonstrated by solving a series of benchmark problems comparing not only the best designs found, but also the statistics of 100 independent runs of the algorithm. The numerical analysis showed that the BSA provided promising results for the analyzed problems. Moreover, in several cases, it was also able to improve the statistics of the independent runs such as the mean and coefficient of variation values.

show abstract

Adaptive dimensional search: A new metaheuristic algorithm for discrete truss sizing optimization

Cited by 122 publications

References 16 publications

An evolutionary-based optimization algorithm for truss sizing design

An evolutionary-based optimization algorithm for truss sizing design

Extraction of Structural System Designs from Topologies via Morphological Analysis and Artificial Intelligence

A Backtracking Search Algorithm for the Simultaneous Size, Shape and Topology Optimization of Trusses

Contact Info

Product

Resources

About