Structural topology optimization using ant colony methodology

Kaveh, A.; Hassani, Behrooz; Shojaee, Saeed; Tavakkoli, S. M.

doi:10.1016/j.engstruct.2008.02.012

Cited by 130 publications

(58 citation statements)

References 18 publications

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“…198.064 (6) 252.258 (7) 285.161 (8) 363.225 (9) 388.386 (10) 494.193 (11) 506.451 (12) 641.289 (13) 645.160 (14) 729.031 (15) 792.256 (16) 816.773 (17) 939.998 (18) 1008.385 (19) 1045.159 (20) 1161.288 (21) 1283.868 (22) 1374.191 (23) 1535.481 (24) 1690.319 (25) 1696.771 (26) 1858.061 (27) 1890.319 (28) 1993.544 (29) 2180.641 (30) 2238.705 (31) 2290.318 (32) 2341.931 (33) 2477.414 (34) 2496.769 (35) 2503 …”

Section: The 72-bar Spatial Truss Exampleunclassified

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Hu³

et al. 2017

Mathematical Problems in Engineering

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Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short) is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

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Section: The 72-bar Spatial Truss Exampleunclassified

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Hu³

et al. 2017

Mathematical Problems in Engineering

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“…Therefore, it is expected that the proposed algorithm can effectively be applied to dynamic topology optimization to improve the convergence rate. 법으로부터 자연 모방 최적화 방법인 입자 군집 최적화 (particle swarm optimization: PSO) [3] , 개미 군집 최적화(ant colony optimization: ACO) [4] 그리고 인공벌 군집 알고리즘 (artificial bee colony algorithm: ABCA) [5] 등이 있다.…”

Section: Article Historyunclassified

Improvement of Topology Algorithm's Convergence Rate Using Chaotic Map

Kim¹,

Kim²,

Lee³

et al. 2014

Journal of the Korean Society of Manufacturing Technology Engin

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ARTICLE INFO ABSTRACT Article history:Recently, a topology algorithm based on the artificial bee colony algorithm (ABCA) has been proposed for static and dynamic topology optimization. From the results, the convergence rate of the algorithm was determined to be slightly slow. Therefore, we propose a new search method to improve the convergence rate of the algorithm using a chaotic map. We investigate the effect of the chaotic map on the convergence rate of the algorithm in static and dynamic topology optimization. The chaotic map has been applied to three cases, namely, employ bee search, onlooker bee search, and both employ bee as well as onlooker bee search steps. It is verified that the case in which the logistic function of the chaotic map is applied to both employ bee as well as onlooker bee search steps shows the best dynamic topology optimization, improved by 5.89% compared to ABCA. Therefore, it is expected that the proposed algorithm can effectively be applied to dynamic topology optimization to improve the convergence rate. 법으로부터 자연 모방 최적화 방법인 입자 군집 최적화 (particle swarm optimization: PSO) [3] , 개미 군집 최적화(ant colony optimization: ACO) [4] 그리고 인공벌 군집 알고리즘 (artificial bee colony algorithm: ABCA) [5] 등이 있다.그 동안의 연구에서 Karaboga와 Basturk [6,7] 그리고 Omkar 등 의 카오틱 제국주의자

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“…Ant colony optimization similar to genetic algorithm is a good choice for structural topology optimization due to their discrete characteristics. Recently some of researchers have employed ant algorithms for topology optimization of continuous and discrete structures (Kaveh et al 2008;Luh and Lin 2008).…”

Section: Introductionmentioning

confidence: 99%

Reliability-based topology optimization of double layer grids using a two-stage optimization method

Mashayekhi

Salajegheh

et al. 2011

Struct Multidisc Optim

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This paper presents a two-stage optimization method for reliability-based topology optimization (RBTO) of double layer grids that considers uncertainties in applied loads. The optimization is performed using a two-stage optimization by employing the method of moving asymptotes (MMA) and ant colony optimization (ACO), which is called MMA-ACO method. For implementation of MMA-ACO, the structural stiffness is maximized using MMA, first. Then, the results of MMA are used to enhance ACO through the following four modifications: (I) finding the structural importance rate of elements or joints and using this to achieve a better topology, (II) determining the number of compressive and tensile element types, (III) changing the lower limit of available cross-sectional areas for the elements of each group and (IV) modifying the generation of random stable structures. In reliability analysis, multiple criteria i.e. stiffness and eigenvalue are considered where the probability of failure in each mode is calculated by Monte Carlo simulation (MCS). To reduce the computational time, the eigenvalues are evaluated using the third order approximation (TOA). Through numerical examples, reliability-based topology designs of typical double layer grids are obtained by ACO and MMA-ACO methods. The numerical results reveal the computational advantages and effectiveness of the proposed MMA-ACO method for the RBTO of double layer grids. Also, the importance of considering uncertainties is then demonstrated by comparing the results obtained by those of other failure probabilities.

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Structural topology optimization using ant colony methodology

Cited by 130 publications

References 18 publications

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Improvement of Topology Algorithm's Convergence Rate Using Chaotic Map

Reliability-based topology optimization of double layer grids using a two-stage optimization method

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