Ray optimization for size and shape optimization of truss structures

Kaveh, A.; Khayatazad, Mojtaba

doi:10.1016/j.compstruc.2012.12.010

Cited by 124 publications

(43 citation statements)

References 22 publications

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“…In addition, the EM-MS algorithm is much better than all other methods in terms of average, standard deviation, and worst results. As it can be seen, the worst design yielded by the EM-MS algorithm is also lighter than the designs obtained by the PSO [36], BB-BC [25], RO [30], EHS [23], and SAHS [23] methods.…”

Section: A 72-bar Spatial Truss Structurementioning

confidence: 80%

“…The optimum designs obtained by the EM-MS algorithm and some other previous studies reported in the literature such as the PSO [37], BB-BC [25], RO [30], EHS [23], SAHS [23], TLBO [27], and CBO [31] methods are presented in Table 8. From the comparison, it is noticed that the EM-MS algorithm gives better design as compared to the PSO [37], BB-BC [25], RO [30], EHS [23], SAHS [23], and CBO [31] , but slightly heavier design when compared with the TLBO [27] method. However, the number of structural analyses for the EM-MS algorithm is relatively less than the TLBO [27] method.…”

Section: A 72-bar Spatial Truss Structurementioning

confidence: 99%

“…where E = the modulus of elasticity; Perez and Behdian [37] Camp [25] Kaveh and Khayatazad [30] Degertekin [23] Degertekin and Hayalioglu [27] Kaveh and Mahdavi [31] No. of analyses 20,000 10,000…”

Section: A 582-bar Tower Truss Structurementioning

confidence: 99%

“…Kaveh and Khayatazad [30] employed RO to size and shape optimization of truss structures. In RO, each solution is modeled as a ray of light that moves in the search space in order to find optimum solution.…”

Section: Introductionmentioning

confidence: 99%

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Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

Jalili

Hosseinzadeh

2017

Period. Polytech. Civil Eng.

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Hybrid electromagnetism-like mechanism and migration strategy (EM-MS) algorithm is a recently developed optimization method which tries to benefit from both electromagnetism-like mechanism (EM) algorithm and migration strategy (MS). IntroductionAttaining optimum designs for structures has been in the focus of wide attention over past years and has established its position as one of the main optimization problems in structural engineering domain. However it is very widely believed that, for many structures with the large number of elements, searching optimum designs is very extreme hardness and sometimes completely time consuming procedure. Hence, extensive studies have been carried out to develop different optimization methods, ranging from gradient-based search techniques to derivative-free global optimization algorithms. As an alternative to the classical optimization approaches, meta-heuristic optimization techniques such as harmony search (HS) [7] have been widely utilized and improved to solve structural optimization problems characterized by non-convex, dis-continuous, and non-differentiable [8][9][10][11][12][13][14][15][16].The meta-heuristic algorithms have some advantages such as a simple framework and ease of implementation. Therefore, these algorithms have been adopted by researchers so far and are well suited to solve various structural optimum design problems including the sizing, layout, and topology optimization problems [17][18][19][20][21]. Due to probabilistic nature of the meta-heuristics, they do not guarantee finding global optimum solutions for any kind of the problems. However, if they properly implemented, meta-heuristics can provide near-optimal or optimal solutions with higher qualities. In designing efficient meta-heuristic algorithms, the exploration and exploitation are extremely important mechanisms. The exploration mechanism is related to the ability of exploring many and different regions of the search space, while the exploitation mechanism is related to the reduction of the diversity by focusing on the individuals with higher fitness to obtain high quality solutions. Therefore, the adequate balance between the exploration and exploitation mechanisms is a vital issue for these algorithms to be effectively executed. To this end, numerous standard and hybrid meta-heuristic algorithms have been applied and developed to optimum design of structures. Some of them will be mentioned below.

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Section: A 72-bar Spatial Truss Structurementioning

confidence: 80%

Section: A 72-bar Spatial Truss Structurementioning

confidence: 99%

Section: A 582-bar Tower Truss Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

Jalili

Hosseinzadeh

2017

Period. Polytech. Civil Eng.

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“…Pareto optimization has been in use ever since. Different optimization algorithms and techniques are used to solve structural optimization problem, like Ray optimization [14], artificial bee colony algorithm [17], particle swarm optimization [10,13,16], genetic algorithm [11,15], ant colony optimization [12,13] etc.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Programming Technique for Solving Multi-objective Structural Problem

Deya¹,

Roy²

2014

IJEM

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This paper proposes a new fuzzy multi-objective optimization approach to solve a multi-objective nonlinear programming problem in context of a structural design. We have been developed a multi-objective structural problem of a planar truss structural model in fuzzy environment. Here, the objectives are (i) to minimize weight of the structure and (ii) to minimize the vertical deflection at loading point. In this model, the design variables are the cross-section of the truss members and the constraints are the stresses in members. This approach is used to solve the structural model under uncertainty based on different operator. A numerical illustration is given to support our approach.

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Constrained mean‐variance mapping optimization for truss optimization problems

Aslani

Ghasemi

Gandomi

2017

Structural Design Tall Build

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Summary Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.

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Ray optimization for size and shape optimization of truss structures

Cited by 124 publications

References 22 publications

Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

A Fuzzy Programming Technique for Solving Multi-objective Structural Problem

Constrained mean‐variance mapping optimization for truss optimization problems

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