Multi-class teaching–learning-based optimization for truss design with frequency constraints

Farshchin, Mohammad; Camp, Charles V.; Maniat, Mohsen

doi:10.1016/j.engstruct.2015.10.039

Cited by 93 publications

(33 citation statements)

References 21 publications

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“…Pholdee and Bureerat (2012), (2013) minimized the weight of the truss subject to displacement constraints. Farshchin et al (2016), Kaveh and Ilchi Ghazaan (2015) and Khatibinia and Sadegh Naseralavi (2014) optimized the weight of the truss with frequency constraints. The geometry, node numbering, material numbering and group numbers of the 200-bar truss are shown in Figure 11.…”

Section: A 200-bar Planar Truss Structurementioning

confidence: 99%

An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

Vo-Duy

Duong-Gia

Ho-Huu

et al. 2019

Int. J. Comput. Methods

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This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a single-loop deterministic method (SLDM) into the nondominated sorting genetic algorithm II (NSGA-II) algorithm to give the so-called SLDM-NSGA-II. Thanks to the advantage of SLDM, the probabilistic constraints are treated as approximating deterministic constraints. And therefore the reliability-based multi-objective optimization problems can be transformed into the deterministic multi-objective optimization problems of which the computational cost is reduced significantly. In these reliability-based multi-objective optimization problems, the conflicting objective functions are to minimize the weight and the displacements of the truss. The design variables are cross-section areas of the bars and contraints include static and dynamic constraints. For reliability analysis, the effect of uncertainty of parameters such as force, added mass in the nodes, material properties and cross-section areas of the bars are taken into account. The effectiveness and reliability of the proposed method are demonstrated through three benchmark-type truss structures including a 10-bar planar truss, a 72-bar spatial truss and a 200-bar planar truss. Moreover, the influence of parameters on the reliability-based Pareto optimal fronts is also carried out.

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Section: A 200-bar Planar Truss Structurementioning

confidence: 99%

An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

Vo-Duy

Duong-Gia

Ho-Huu

et al. 2019

Int. J. Comput. Methods

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“…The result comparison between BBO‐DE and other latest methods in literature, including PSO, DPSO, MC‐TLBO, EBBO, OMGSA, HRPSO, SGA, and VPS, is shown in Table . According to Table , the proposed method yields a structural weight of 359.8231 kg, whereas it is 377.20, 360.40, 359.966, 359.86, 359.97, 364.72, 359.93, and 359.94 kg for PSO, DPSO, MC‐TLBO, EBBO, OMGSA, HRPSO, SGA, and VPS methods, respectively. It may also be seen from the table that BBO‐DE is capable of generating better quality solutions than PSO, DPSO, MC‐TLBO EBBO, OMGSA, and HRPSO methods as reflected from its standard deviation, best weight, and average weight.…”

Section: Numerical Testsmentioning

confidence: 99%

“…In some cases, they suffer from premature convergence as well as huge computational effort. Hence, many improved and hybridized versions of abovementioned metaheuristic methods have been developed by researchers to solve structural optimum design problems with discrete and continuous variables, such as heuristic PSO (HPSO), hybrid PSO and swallow swarm optimization (HPSSO), self‐adaptive harmony search (SAHS), modified TLBO, enhanced CBO (ECBO), hybrid electromagnetism‐like mechanism and migration strategy (EM‐MS), chaotic BBO (CBBO), democratic PSO (DPSO), multiclass TLBO (MC‐TLBO), orthogonal multigravitational search algorithm (OMGSA), improved magnetic charged system search (IMCSS), hybrid CSS algorithm and migration‐based local search (CSS‐MBLS), accelerated multigravitational search algorithm (AMGSA), enhanced BBO, improved dolphin echolocation algorithm (IDE), discrete gravitational search algorithm (DGSA), improved gravitational search algorithm, and hybrid improved gravitational search algorithm and orthogonal crossover (IGSA‐OC) …”

Section: Introductionmentioning

confidence: 99%

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

Jalili

Hosseinzadeh

2018

Structural Design Tall Build

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This paper presents a hybrid BBO-DE algorithm by hybridizing biogeography-based optimization (BBO) and differential evolution (DE) methods for optimum design of truss structures with continuous and discrete variables. In BBO-DE, the migration operator of BBO method serves as a local exploiter mechanism during the search process. Besides, DE has a role of the global exploration by performing multiple search directions in the search space to preserve more diversity in the population. By embedding of DE algorithm in BBO method as a mutation mechanism, the balance between the exploration and exploitation abilities is further improved. The comparative results with some of the most recently developed methods demonstrate the fast convergence properties of the proposed algorithm and confirm its effectiveness to solve optimum design problems of truss structures with continuous and discrete variables. KEYWORDSbiogeography-based optimization, continuous, differential evolution, discrete, optimum design, truss structures | INTRODUCTIONIn recent years, increasing the consumption of materials in the building sector and the limitation of natural resources have intensified competitions among the structural engineers to such an extent that attaining optimum designs for structures is more vital now than ever before. Structural optimization has achieved a great advance as a result of modern computers entering the stage. By using today's computational machines, a large-scale structure with complex geometry and thousands of members can be iteratively processed to get a more economical design. For such structures, the number of design variables is high and the optimum design procedure may require huge computational effort. As a result, developing efficient optimization techniques to achieve a fast convergence rates with more accurate results is one of the active research topics in the field of structural optimization. In the sizing of truss structures, the cross-sectional areas of structural members are considered as design variables, and the design constraints are some limitations imposed on the stresses and displacements as well as free vibrational frequencies. The objective function is generally considered as to minimize the weight or volume of the structure. However, there are many critical factors that can affect the objective function or costs of a structure.From the optimization point of view, the sizing of truss structures can be classified into two main types based on design variables: continuous and discrete. Mathematical optimization techniques have some features that are later found to be not very much suitable for practical structural design optimization problems, [1] such as assuming of continuous design variables, requiring gradient computations of the objective function and constraints, and depending on the initial estimate of solution vector. These methods have some serious difficulties when it is required to assign cross sections from a discrete set of practically available sections. Moreover, when both of the nodal ...

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“…Kaveh and Mahdavi (2014) studied a colliding-bodies optimization (CBO). Tejani et al (2016b) suggested a modified sub-population teaching-learningbased optimization (MS-TLBO) and Farshchin et al (2016) used Multi-Class TLBO (MC-TLBO) for trusses subjected to frequency bounds. Kaveh and Zolghadr (2017) used tug of war optimization (TWO), whereas Kaveh and Ilchi Ghazaan (2017) used vibrating particles system (VPS).…”

Section: Introductionmentioning

confidence: 99%

Truss optimization with natural frequency bounds using improved symbiotic organisms search

Tejani

Savsani

Patel

et al. 2018

Knowledge-Based Systems

131

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Many engineering structures are subjected to dynamic excitation, which may lead to undesirable vibrations. The multiple natural frequency bounds in truss optimization problems can improve dynamic behaviour of structures. However, shape and size variables with frequency bounds are challenging due to its characteristic, which is non-linear, non-convex, and implicit with respect to the design variables. As the main contribution, this work proposes an improved version of a recently proposed Symbiotic Organisms Search (SOS) called an Improved SOS (ISOS) to tackle the abovementioned challenges. The main motivation is to improve the exploitative behaviour of SOS since this algorithm significantly promotes exploration which is a good mechanism to avoid local solution, yet it negatively impacts the accuracy of solutions (exploitation) as a consequence. The feasibility and effectiveness of ISOS is studied with six benchmark planar/space trusses and thirty functions extracted from the CEC2014 test suite, and the results are compared with other meta-heuristics. The experimental results show that ISOS is more reliable and efficient as compared to the basis SOS algorithm and other state-of-the-art algorithms.

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Multi-class teaching–learning-based optimization for truss design with frequency constraints

Cited by 93 publications

References 21 publications

An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods

Truss optimization with natural frequency bounds using improved symbiotic organisms search

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