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multi-objective structural optimization for the automatic member grouping of truss structures using evolutionary algorithms

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COMPUTERS & STRUCTURES 2024年 292卷

作者： Carvalho, Jose Pedro G. Vargas, Denis E. C. Jacob, Breno P. Lima, Beatriz S. L. P. Hallak, Patricia H. Lemonge, Afonso C. C. Univ Fed Rio de Janeiro Civil Engn Program COPPE Rio De Janeiro Brazil Fed Ctr Technol Educ Minas Gerais Dept Math Belo Horizonte Brazil Univ Fed Juiz de Fora Sch Engn Dept Appl & Computat Mech Juiz De Fora Brazil

This paper aims to formulate the multi-objective structural optimization problem to find the best member group-ing of truss structures. The weight of the structure and the different number of discrete cross-sectional areas are the conflicting objective functions to be minimized simultaneously, generating a Pareto front presenting the non-dominated solutions. Sixteen multi-objective evolutionary algorithms are adopted to solve the proposed optimization problems. Several benchmark problems and a new proposed optimization problem are analyzed. Even though original groupings based on symmetry are defined, it is possible to discover other competitive structural member configurations that can interest decision-makers regarding manufacturing, cutting, transportation, checking, and welding.

关键词： Automatic member grouping multi-objective structural optimization Differential evolution algorithms Cardinality constraints

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multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2021年第6期93卷 1029-1042页

作者： Ozkan, Ramazan Genc, Mustafa Serdar Erciyes Univ Dept Energy Syst Engn Wind Engn & Aerodynam Res Lab Kayseri Turkey Mugla Sitki Kocman Univ Dept Energy Syst Engn Kotekli Turkey Erciyes Univ Energy Convers & Res & Applicat Ctr Kayseri Turkey Erciyes Univ Applicat Ctr Kayseri Turkey MSG Teknol Ltd S Ti Kayseri Turkey

Purpose Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine. Design/methodology/approach To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid-structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade. Findings As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production. Originality/value ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.

关键词： Fluid-structure interaction Composite materials multi-objective structural optimization Wind energy systems

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multi-objective structural optimization of a Composite Wind Turbine Blade Considering Natural Frequencies of Vibration and Global Stability

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ENERGIES 2023年第8期16卷 3363页

作者： Couto, Lucas de Landa Moreira, Nicolas Estanislau Saito, Josue Yoshikazu de Oliveira Hallak, Patricia Habib Lemonge, Afonso Celso de Castro Univ Fed Juiz de Fora Fac Engn Dept Mech Engn BR-36036900 Juiz De Fora Brazil Univ Fed Juiz de Fora Fac Engn Dept Appl & Computat Mech BR-36036900 Juiz De Fora Brazil

Aspects concerning resonance and global stability of a wind turbine blade must be carefully considered in its optimal design. In this paper, a composite wind turbine blade with an external geometry based on the NREL 5 MW model was subjected to multi-objective structural optimization considering these aspects. Four multi-objective structural optimization problems are formulated considering the blade mass, the maximum blade tip displacement, the natural frequencies of vibration, and the critical load factor as objective functions. The design variables are the number of plies, material, and fiber orientation. The design constraints are the materials' margin of safety, the blade's allowable tip displacement, and the minimum load factor. The blade model is submitted to the loads determined by the actuator lines theory and discretized in a finite element parameterized model using the Femap software according to geometric design variables. Among many multi-objective evolutionary algorithms available in the literature concerning evolutionary computation, the NSGA-II is the adopted evolutionary algorithm to solve the multi-objective optimization problems. Pareto fronts are obtained and performance indicators are used to evaluate the distribution of the non-dominated solutions. multi-criteria decision-making is used to extract the solutions from the Pareto fronts according to the decision-maker's preferences. The values of the objective functions, design variables, and constraints are presented for each extracted solution. The proposed study is expected to contribute to the multi-objective optimization and the structural design of wind turbine blades.

关键词： wind turbine blade composite material multi-objective structural optimization non-dominated sorting genetic algorithm II

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An interactive reference-point-based method for incorporating user preferences in multi-objective structural optimization problems

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APPLIED SOFT COMPUTING 2024年 165卷

作者： Vargas, Denis E. C. Lemonge, Afonso C. C. Barbosa, Helio J. C. Bernardino, Heder S. Fed Ctr Technol Educ Minas Gerais CEFET MG Dept Math Belo Horizonte MG Brazil Fed Univ Juiz de Fora UFJF Dept Appl & Computat Mech Fac Engn Juiz de Fora MG Brazil Univ Fed Juiz de Fora UFJF Inst Exact Sci Dept Comp Sci Juiz de Fora MG Brazil Natl Lab Sci Comp LNCC Petropolis RJ Brazil

Although multi-objective evolutionary algorithms (MOEAs) are often used to solve multi-objective structural optimization problems (MOSOPs), the incorporation of decision-maker (DM) preferences into MOEAs to search for regions of interest from the Pareto front has received limited attention in these problems. Recently, three MOEAs have shown promising results in solving MOSOPs incorporating DM's preferences based on a priori reference points. However, providing this information can be challenging as the DM may not know an initial reference point for the MOSOP. Therefore, this study analyzes three interactive multi-objective optimization (IMO) algorithms, called I-NSGA-II, I-GDE3, and I-GDE3+APM, in which the reference points are progressively incorporated during the search process. These IMOs are applied to benchmark MOSOPs and evaluated using well-known performance indicators. The results demonstrate that I-NSGA-II outperforms IGDE3 and I-GDE3+APM, which have similar overall performances. Additionally, the IMOs considered here outperformed their respective a priori approaches in MOSOPs where the reference points are located at the central sector of the Pareto front. Also, we identified the performance indicators in which the approaches considered in this work performed better than their corresponding a priori ones. Furthermore, the research findings suggest that these IMOs can generate good results without defining a priori reference points, making them a promising approach for solving MOSOPs and enabling DMs to make better and more reliable decisions.

关键词： multi-objective structural optimization Interactive preference information Reference point Differential evolution Adaptive penalty method

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Solving multi-objective structural optimization problems using GDE3 and NSGA-II with reference points

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ENGINEERING STRUCTURES 2021年 239卷 112187-112187页

作者： Vargas, Denis E. C. Lemonge, Afonso C. C. Barbosa, Helio J. C. Bernardino, Heder S. Fed Ctr Technol Educ Minas Gerais Dept Math Belo Horizonte MG Brazil Univ Fed Juiz de Fora Dept Appl & Computat Mech Fac Engn Juiz De Fora Brazil Univ Fed Juiz de Fora Inst Exact Sci Dept Comp Sci Juiz De Fora Brazil Natl Lab Sci Comp Petropolis Brazil

structural optimization problems can be described as multi-objective optimization problems (MOOPs) due to the presence of multiple conflicting objectives. Recently, procedures based on the decision-maker's preferences have attracted the interest of researchers. This type of information allows for the search technique to focus only on regions of interest instead of all possible solutions. Despite its advantage, incorporating these preferences into the search engine remains scarcely explored in multi-objective structural optimization design problems (MOSOPs). We propose here solving MOSOPs using multi-objective meta-heuristics guided by reference points. Particularly, variants where the decision-maker's preferences drive the search of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the third evolution step of Generalized Differential Evolution (GDE3), and GDE3 + APM, which is GDE3 equipped with a constraint handling technique called Adaptive Penalty Method (APM), are considered. These approaches are labelled respectively as R-NSGA-II, R-GDE3, and R-GDE3 + APM. It is relevant to highlight that the R-GDE3 and R-GDE3 + APM are proposed in this paper. The results indicate that all were competitive and generated good solutions when solving the MOSOPs considered here. In addition, R-NSGA-II outperformed the remaining techniques in the computational experiments.

关键词： multi-objective structural optimization Preferences information Reference points Differential evolution Adaptive penalty method

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multi-objective truss structural optimization considering natural frequencies of vibration and global stability

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 165卷 113777-113777页

作者： Lemonge, Afonso C. C. Carvalho, Jose P. G. Hallak, Patricia H. Vargas, Denis E. C. Univ Fed Juiz de Fora Fac Engn Dept Appl & Computat Mech Juiz De Fora Brazil Univ Fed Juiz de Fora Fac Engn Postgrad Program Civil Engn Juiz De Fora Brazil Southeast Fed Inst Educ Sci & Technol Minas Gerai Rio Pomba Brazil

Conflicting objectives such as minimizing weight and minimizing the maximum nodal displacement, with constraints on normal stresses in the bars, is a common multi-objective structural optimization problem widely found in the literature. This paper proposes multi-objective structural optimization problems with the combination of new conflicting objectives functions and constraints, such as the natural frequencies of vibration and the load factors concerning the global stability of the structure. The solution for these problems may be of great interest in the field of structural engineering, not yet discussed in the literature. The problems analyzed in this paper deal with both discrete and continuous sizing, shape, and layout design variables. The search algorithm adopted here is a modified version of the Differential Evolution called the Third Evolution Step Differential Evolution (GDE3). Several experiments are analyzed with their Pareto-fronts showing the non-dominated solutions. The solutions are defined after obtaining the Pareto curve, which is one of the most important steps and a task that may not be trivial for the Decision Maker. This paper involves a strategy that establishes criteria defining weights (importance) for each objective function and, through these values, enables comparison scenarios. The numerical experiments include plane and spatial benchmark trusses. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： multi-objective structural optimization Differential evolution Natural frequencies of vibration Global stability

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ULTIMATE LIMIT STATE BASED SHIP structural DESIGN USING multi-objective DISCRETE PARTICLE SWARM optimization 34

ULTIMATE LIMIT STATE BASED SHIP STRUCTURAL DESIGN USING MULT...

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34th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE2015)

作者： Ma, Ming Hughes, Owen McNatt, Tobin DRS Technol Inc Adv Marine Technol Ctr Stevensville MD 21666 USA Virginia Tech Aerosp & Ocean Engn Blacksburg VA USA

ISBN: (纸本)9780791856499

multi-objective optimization problems consist of several objectives that must be handled simultaneously. These objectives usually conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Genetic or evolution algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. Among many algorithms, the particle swarm optimization (PSO) has been found to be faster with less computational overhead. In this paper a multi objective discrete particle swarm optimization is formulated and used to optimize a large and complex thin-wall structure on the basis of weight, safety and cost. The structure weight and cost are calculated using realistic finite element models. The design process has two stages: (1) the actual stresses are obtained by finite element analysis of the full ship, (2) for a midship segment of the ship (referred to as a "control cluster") the structural safety is evaluated using the ALPS/ULSAP set of ultimate limit state criteria, and then the segment is optimized using any suitable optimization method (in this paper, the PSO method). Both stages involve iteration, but the process is arranged so as to keep the number of full ship finite element analyses to a minimum. The complete design process is illustrated for a 200,000 ton oil tanker. The numerical results show that the PSO method is very useful to perform ultimate strength based ship structural optimization with multi objectives, namely minimization of the structural weight and cost and maximization of structural safety. The example also demonstrates that the proper definition of boundary conditions and design load cases is of paramount importance fo

关键词： Stiffened panel multi-objective structural optimization Particle swarm optimization ultimate limit states MAESTRO ALPS

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A Computationally Efficient Framework for Inelastic multi-objective Performance-Based Seismic Design Using Surrogate Linear Dynamic Models

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-19页

作者： Mokarram, Vahid Banan, Mohammad Reza Department of Mechanical Engineering Faculty of Engineering Jahrom University Fars Jahrom 74131-88941 Iran Department of Civil and Environmental Engineering School of Engineering Shiraz University Fars Shiraz 71348-51156 Iran

This study presents a robust framework for automatic multi-objective performance-based seismic design (PBSD) of buildings, balancing computational efficiency and optimal design outcomes. PBSD aims to elevate structural resilience against seismic events by judiciously balancing safety and economic considerations. The overarching objective of PBSD is ideally addressed within a multi-objective optimization (MOO) framework, utilizing inelastic time-history analysis as the evaluation tool. Although the computational demands of such an approach are significant, prior research indicates that relying on approximate pushover analyses in the MOO process can lead to unreliable solutions. This paper introduces the extended fast converging multi-objective particle swarm optimization (FC-MOPSO) algorithm, which offers four alternative MOO procedures—namely, the Direct Nonlinear Dynamic Procedure (NDP), the Direct Linear Dynamic Procedure (LDP), the Surrogate LDP, and the Surrogate Nonlinear Static Procedure (NSP) models—to achieve reliable inelastic time-history-based optimal Pareto fronts within the PBSD framework. The effectiveness of the proposed models is demonstrated through the MOO design of three steel moment-resisting frames (MRFs) with setbacks. The results showcase substantial reductions in computational costs for the Direct and Surrogate LDP models compared to the Direct NDP model, thereby making them practical for engineering practitioners. Furthermore, the findings reveal that when the optimal design is selected through a Pareto front based on the two concurrent objectives—minimizing both the initial cost and the life-cycle cost—the Direct and Surrogate LDP models outperform the Surrogate NSP model. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Costly optimization problems Inelastic time-history analysis Irregular buildings multi-objective structural optimization Performance-based seismic design

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Hierarchically decomposed multi-level optimization for ship structural design 35

Hierarchically decomposed multi-level optimization for ship ...

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35th ASME International Conference on Ocean, Offshore and Arctic Engineering

作者： Ma, Ming Paik, Jeom Kee McNatt, Tobin DRS Technol Inc Adv Marine Technol Ctr Stevensville MD 21666 USA Pusan Natl Univ Korea Ship & Offshore Res Busan South Korea

ISBN: (纸本)9780791850008

As the demand for modern ships grows to meet greater reliability, fuel efficiency, and economy, the interest in reducing structural weight while enhancing safety has also increased. Ship structural optimization is usually a mixed discrete continuous design problem constrained by buckling and material strength, and involves the optimization of a large number of variables such as (continuous/discrete) plate thickness, scantlings of stiffeners and frames, and the (discrete) number of stiffeners and frames. Further complication arises when the structure is constrained by buckling and strength under compression and subject to practical design rules. Although 3D finite element analysis are widely used for full ship structure stresses and limit state analysis, their application for structural optimization is still prohibitive due to the computational cost. In this paper a multi-level heuristic based multi-objective optimization is formulated and used to optimize a large and complex thin-wall structure on the basis of weight, and safety. The optimization procedure has three levels: (1) For each stiffened panel, a heuristic based multi-objective optimization method is used to minimize its weight and maximize its safety, and a Pareto front is obtained. The ALPS/ULSAP ultimate limit state criteria, which is parametric formulated, mesh free, computational efficient, and is able to predict six different failure modes for a stiffened panel, are used to formulate the safety objective function. (2) Use the Pareto front of each panel as a gene pool to optimize the hull girder global properties, such as hull girder cross section area moment of inertia and vertical center of gravity (VCG). (3) Minimize the difference between the design variables from the local optimization and the global optimization by iterating 3D finite element analysis of the full ship. An example of optimizing the longitudinal structures of a full cargo hold of a 200,000 ton oil tanker is presented. The numerical

关键词： Stiffened panel multi-objective structural optimization collaborative optimization ultimate limit states MAESTRO ALPS

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Optimizing earthquake design of reinforced concrete bridge infrastructures based on evolutionary computation techniques

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structural AND multiDISCIPLINARY optimization 2020年第3期61卷 1087-1105页

作者： Camacho, Vitor T. Horta, Nuno Lopes, Mario Oliveira, Carlos S. Inst Super Tecn Dept Civil Engn CERIS DECivil Av Rovisco Pais 1 P-1049001 Lisbon Portugal Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

This paper presents a methodology for the earthquake design of reinforced concrete (RC) bridge infrastructures based on the application of multi-objective evolutionary techniques. The purpose of the methodology is to allow better decision-making for the earthquake design of bridges by proposing optimized solutions which offer trade-offs between material quantities, performance/robustness, and cost. For this, two multi-objective problems (MOPs) were defined with two sets of objectives: the first objective set optimizes the amount of material used in the piers;the second set of objectives comprises a cost function and a performance metric. The NSGA-II algorithm was adapted and applied with real coded variables and multiple non-linear dynamic analyses performed in each fitness evaluation. The results of the runs show different Pareto fronts strongly associated with solution schema of pier-deck connections and steel distribution between piers. The results also allow to perceive the influence of ductility through the impact that certain variables in certain piers have on the performance of solutions. The importance of support conditions/connections between infrastructure and deck and of confinement of piers is clear. The results of the two MOPs show that sub-optimal solutions in terms of volume of materials used may be interesting in terms of reliability gain. In the end, the results of applying the methodology present solutions which offer trade-offs and information gain valuable for bridge design.

关键词： Evolutionary algorithms multi-objective structural optimization Bridge earthquake design Non-linear dynamic analysis

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