Discrete optimum design of truss structures using artificial bee colony algorithm

Sönmez, M. Kemal

doi:10.1007/s00158-010-0551-5

Cited by 161 publications

(59 citation statements)

References 16 publications

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“…Meanwhile, the optimization performance of ABC is comparable and sometimes superior to the state-of-the-art meta-heuristics. Therefore, ABC has aroused much interest and has been successfully applied to different kinds of optimization problems [27][28][29].…”

Section: The Artificial Bee Colony Algorithmmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Zhang

2011

Entropy

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Due to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP) has not received sufficient attention. In this paper, we propose an artificial bee colony (ABC) algorithm for SJSSP with the objective of minimizing the maximum lateness (which is an index of service quality). First, we propose a performance estimate for preliminary screening of the candidate solutions. Then, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation (through Monte Carlo simulation) process. Finally, the computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.

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Section: The Artificial Bee Colony Algorithmmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Zhang

2011

Entropy

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“…The application of different optimization technique to structural problem has attracted the interest of many researchers. For example Ray Optimization 6 , artificial bee colony algorithm 16 , Particle Swarm Optimization 6,7,12,14,15,17 2007)). Fuzzy as well as intuitionistic fuzzy optimization in case of structural engineering not only helps the engineers in their design and analysis of systems but also leads to significant advances and new discoveries in fuzzy optimization theory and technique.…”

Section: Introductionmentioning

confidence: 99%

Truss Design Optimization with Imprecise Load and Stress in Intuitionistic Fuzzy Environment

Sarkar¹,

Roy²

2017

jusps-B

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In this paper, we have introduced a intuitionistic fuzzy mathematical programming with intuitionistic fuzzy number as co-efficient of objectives. Real world engineering problems are usually designed with imprecise parameter by the presence of many conflicting objectives. In this paper we develop an approach to solve multi-objective structural design using probabilistic operator. Here total integral values of triangular intuitionistic fuzzy number,and it has been used for imprecise applied load and material density in the test problem. In this paper we have considered a multi objective structural optimization model with weight and deflection as objectives and stress as constraint function. Here design variables are considered as cross sectional area of bars. This classical truss design example is presented here in to demonstrate the significance of our proposed optimization approach. Numerical example is given here to illustrate this structural model through this approximation method.

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“…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.

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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.

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Discrete optimum design of truss structures using artificial bee colony algorithm

Cited by 161 publications

References 16 publications

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

Truss Design Optimization with Imprecise Load and Stress in Intuitionistic Fuzzy Environment

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

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