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A multi-objective robust optimization scheme for reducing optimization performance deterioration caused by fluctuation of decision parameters in chemical processes

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COMPUTERS & CHEMICAL ENGINEERING 2018年 119卷 1-12页

作者： Liao Zhiqiang Li Taifu Chen Peng Zuo Shilun Mie Univ Dept Environm Sci & Engn Fac Bioresources Tsu Mie 1577 Japan Chongqing Univ Sci & Technol Dept Elect & Informat Engn Chongqing 401331 Peoples R China Chongqing Univ Sci & Technol Sch Mech Engn Chongqing 401331 Peoples R China

The fluctuation of decision parameters will deviate from the optimal decision, which will have significant impact on the optimization performance of chemical processes. To reduce optimization performance deterioration caused by fluctuation of decision parameters in chemical processes, a multi-objective robust optimization scheme is developed to assess performance robustness. In addition, based on the model that maps decision parameters to objective performance through neural network, a new robustness evaluation metric is created as the fitness value of the multi-objective evolutionary algorithm (for improving the strength Pareto evolutionary algorithm (SPEAII)) to elaborate the relationship between robustness and fluctuation. The efficacy of the proposed method is verified with HCN production process application by comparing with genetic algorithm (GA) and weighted single-objective robust optimization. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Decision parameters fluctuation robustness evaluation metric Chemical process multi-objective robust optimization

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Collision performance and multi-objective robust optimization of a combined multi-cell thin-walled structure for high speed train

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THIN-WALLED STRUCTURES 2019年 135卷 341-355页

作者： Wang, Shiming Peng, Yong Wang, Tiantian Che, Quanwei Xu, Ping Cent S Univ Sch Traff & Transportat Engn Minist Educ Key Lab Traff Safety Track Changsha 410075 Hunan Peoples R China Cent S Univ State Key Lab High Performance Complex Mfg Changsha 410006 Hunan Peoples R China Cent S Univ Joint Int Res Lab Key Technol Rail Traff Safety Changsha 410075 Hunan Peoples R China Cent S Univ Natl & Local Joint Engn Res Ctr Safety Technol Ra Changsha 410075 Hunan Peoples R China

Based on the Simplified Super Folding Element (SSFE) theory, the theoretical prediction of average crushing force (F-avg) for multi-cell thin-walled structures is inferred and a combined five-cell thin-walled structure used in high speed train is proposed and investigated in this paper. The finite element model of the proposed structure and the theoretical prediction are validated by a full scaled impact experiment. Then, parametric studies are performed to evaluate the effects of design variables, including the thickness (t) and the side length (a) of the orthohexagonal cell, on collision responses based on the validated FE model and theoretical prediction. It is found that both specific energy absorption (SEA) and the maximum initial force (F-max) are obviously affected by the design parameters. Particularly, the effect of parameter t on crushing performance is greater than that of parameter a. In further, to minimize the F-max and maximum SEA under the constraint of F-avg a multi-objective robust optimization methodology is adopted. The Optimal Latin Hypercube Design (OLHD) and orthogonal design are combined to perform Design of Experiment (DoE) and dual response surface models (DRSM) are constructed for the optimization. The optimal results of deterministic optimization indicate that the F-avg decreases by 11.07% compared with the original design while the robust optimization optimal result of F-max decreases by 10.01%. However, the robust optimization optimal design is more acceptable considering the robustness, which means the robust optimization is more attractive than deterministic optimization in practical engineering application.

关键词： High speed train multi-cell thin-walled structure Crashworthiness multi-objective robust optimization Dual response surface

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Advanced multi-objective robust optimization Under Interval Uncertainty Using Kriging Model and Support Vector Machine

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2018年第4期18卷 041012-041012页

作者： Xie, Tingli Jiang, Ping Zhou, Qi Shu, Leshi Zhang, Yahui Meng, Xiangzheng Wei, Hua Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China Zhengzhou Univ Sch Mech Engn Zhengzhou 450001 Henan Peoples R China

There are a large number of real-world engineering design problems that are multi-objective and multi-constrained, having uncertainty in their inputs. robust optimization is developed to obtain solutions that are optimal and less sensitive to uncertainty. Since most of complex engineering design problems rely on time-consuming simulations, the robust optimization approaches may become computationally intractable. To address this issue, an advanced multi-objective robust optimization approach based on Kriging model and support vector machine (MORO-KS) is proposed in this work. First, the main problem in MORO-KS is iteratively restricted by constraint cuts formed in the subproblem. Second, each objective function is approximated by a Kriging model to predict the response value. Third, a support vector machine (SVM) classifier is constructed to replace all constraint functions classifying design alternatives into two categories: feasible and infeasible. The proposed MORO-KS approach is tested on two numerical examples and the design optimization of a micro-aerial vehicle (MAV) fuselage. Compared with the results obtained from other MORO approaches, the effectiveness and efficiency of the proposed MORO-KS approach are illustrated.

关键词： interval uncertainty multi-objective robust optimization constraint cuts support vector machine Kriging model

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A novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers

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ELECTRIC POWER SYSTEMS RESEARCH 2019年第Apr.期169卷 115-123页

作者： Yuan, Shuang Dai, Chaohua Guo, Ai Chen, Weirong Southwest Jiaotong Univ Sch Elect Engn Chengdu Sichuan Peoples R China

With the increasing applications of wind energy & power technologies, the stochastic fluctuation and reverse peak load regulation characteristic of wind power brings great challenges to unit commitment of power systems. It is difficult to meet the demand of the peak load regulation with the wind power integration for the existing robust unit commitment models when only considering the cost. In order to solve this problem, an additional objective function of the ability of peak load regulation is constructed for unit commitment solutions. As a result, a novel multi-objective robust unit commitment model considering both the cost and peak load regulation is proposed. Moreover, probabilistic scenario analysis is applied to constraint the model of the fluctuant wind power, and temporal correlation of wind powers is also considered to further improve the economy in the long run. In order to deal with multi-objective problem for the robust optimization solutions, the above-mentioned problem is decomposed into two stages, i.e., a main problem and a subproblem. The main problem is to determine unit commitment and the subproblem is a "min-max optimization" one to obtain the unit outputs. Finally, the simulation analysis based on IEEE118 node system verified the robustness and the validity of the proposed model.

关键词： Peak load regulation multi-objective robust optimization Unit commitment Temporal correlation of wind power

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Min-ordering and max-ordering scalarization methods for multi-objective robust optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期275卷 446-459页

作者： Schmidt, M. Schoebel, Anita Thom, Lisa Erasmus Univ Rotterdam Sch Management Dept Technol & Operat Management POB 1738 NL-3000 DR Rotterdam Netherlands Univ Gottingen Inst Numer & Angew Math Lotzestr 16-18 D-37083 Gottingen Germany

Several robustness concepts for multi-objective uncertain optimization have been developed during the last years, but not many solution methods. In this paper we introduce two methods to find min-max robust efficient solutions based on scalarizations: the min-ordering and the max-ordering method. We show that all point-based min-max robust weakly efficient solutions can be found with the max-ordering method and that the min-ordering method finds set-based min-max robust weakly efficient solutions, some of which cannot be found with formerly developed scalarization based methods. We then show how the scalarized problems may be approached for multi-objective uncertain combinatorial optimization problems with special uncertainty sets. We develop compact mixed-integer linear programming formulations for multi-objective extensions of bounded uncertainty (also known as budgeted or Gamma-uncertainty). For interval uncertainty, we show that the resulting problems reduce to well-known single-objective problems. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： multiple objective programming robust optimization multi-objective robust optimization Combinatorial optimization Scalarization

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The multi-objective robust optimization of the loading path in the T-shape tube hydroforming based on dual response surface model

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期82卷 1595-1605页

作者： Huang, Tianlun Song, Xuewei Liu, Xinyang Jilin Univ State Key Lab Automobile Dynam Simulat Changchun 130025 Peoples R China Case Western Reserve Univ 10900 Euclid Ave Cleveland OH 44106 USA

In this study, a dual response surface model-based multi-objective robust optimization method is introduced to deal with the uncertainties in the tube hydroforming process. The objective of this study is to maximize the protrusion height and minimize the thinning ratio;meanwhile, the variations of the objectives should be minimized. A valid finite element model obtained from experimental result and LS-DYNA is employed to simulate the T-shape tube hydroforming process. To improve computation efficiency, radial basis function combined with Latin hypercube and orthogonal design sampling strategies is employed to construct dual response surface model, which are the mean and standard deviation response of the hydroforming process, respectively. The robust Pareto solutions can be obtained using NSGA-II;meanwhile, the ideal point method is used to obtain the most satisfactory solution from the Pareto solutions for the design engineers. As a conclusion, a significant improvement of the robustness can be achieved;however, the mean performance of the protrusion height has to be sacrificed.

关键词： Tube hydroforming Loading path multi-objective robust optimization Dual response surface model

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Utilizing Kriging Surrogate Models for multi-objective robust optimization of Electromagnetic Devices

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IEEE TRANSACTIONS ON MAGNETICS 2014年第2期50卷 693-696页

作者： Xia, Bin Ren, Ziyan Koh, Chang-Seop Chungbuk Natl Univ Coll Elect & Comp Engn Chungbuk 361763 South Korea Shenyang Univ Technol Sch Elect Engn Liaoning 110870 Peoples R China

This paper presents a multi-objective robust optimization strategy assisted by the surrogate model. In order to guarantee the accurate response prediction, the performances of three different Kriging surrogate models, ordinary Kriging, first-order universal Kriging (UK), and second-order UK, are investigated through analytical benchmark functions. Once the accurate model is constructed, the performance analysis can be efficiently approximated during optimization process. Furthermore, the robustness against uncertainty is evaluated by the worst-case scenario through applying optimization technique to the approximated model in the uncertainty set. The proposed algorithm is validated through one electromagnetic application, a robust version of the TEAM 22.

关键词： Kriging surrogate model multi-objective robust optimization TEAM 22 worst case scenario

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Free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2024年 108卷

作者： Chen, Yugeng Zhong, Rui Wang, Qingshan Chen, Liming Qin, Bin Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China Cent South Univ State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Cent South Univ Sch Traff & Transportat Engn Key Lab Traff Safety Track Minist Educ Changsha 410075 Peoples R China Cent South Univ Joint Int Res Lab Key Technol Rail Traff Safety Changsha 410075 Peoples R China Cent South Univ Natl & Local Joint Engn Res Ctr Safety Technol Rai Changsha 410075 Peoples R China

In this study, the free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters are fulfilled. The numerical model for free vibration of the plate is derived by combining the three-dimensional elasticity theory and Rayleigh-Ritz method, and the validity of the model is illustrated by numerical results. On this basis, considering various uncertainties within the plate, a new uncertainty-propagation analysis method is constructed by integrating the numerical model, interval-analysis model, kriging model and optimization. The one-dimensional and multi-source uncertainty-propagation analysis of the fundamental frequency is finished using this method, and the accuracy is proved by comparing with Monte Carlo simulation results. Meanwhile, to minimize the effects of uncertainties on the performance of plates at the design stage, an objective multi-objective robust optimization model with the objectives of maximizing the fundamental frequency and minimizing the robustness factor is established. Finally, an improved pelican optimization algorithm is proposed by introducing the improved opposition-based learning strategy, tracking strategy with step control and convergence strategy. And the Pareto front and corresponding design schemes applicable to different working environments are obtained without repeating the optimization.

关键词： Three-dimensional pyramidal truss core sandwich plates Interval uncertainty Free vibration multi-objective robust optimization Improved pelican optimization algorithm

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A Sequential multi-objective robust optimization Approach Under Interval Uncertainty Based on Support Vector Machines

A Sequential Multi-Objective Robust Optimization Approach Un...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Xie, Tingli Zhou, Qi Hu, Jiexiang Shu, Leshi Jiang, Ping Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

ISBN: (纸本)9781538609484

Interval uncertainty can cause uncontrollable variations in the objective and constraint values, which could seriously deteriorate the performance or even change the feasibility of the optimal solutions. robust optimization is to obtain solutions that are optimal and minimally sensitive to uncertainty. In this paper, a sequential multi-objective robust optimization (MORO) approach based on support vector machines (SVM) is proposed. Firstly, a sequential optimization structure is adopted to ease the computational burden. Secondly, SVM is used to construct a classification model to classify design alternatives into feasible or infeasible. The proposed approach is tested on a numerical example and an engineering case. Results illustrate that the proposed approach can reasonably approximate solutions obtained from the existing sequential MORO approach (SMORO), while the computational costs are significantly reduced compared with those of SMORO.

关键词： Interval uncertainty multi-objective robust optimization sequential optimization approach support vector machines

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Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities

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APPLIED ENERGY 2023年第1期343卷

作者： Yao, Zhaosheng Wang, Zhiyuan Ran, Lun Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China

The combination of low-carbon electricity and electric vehicles brings considerable economic and environmen-tal benefits but also introduces challenges due to the complexity and uncertainty of system synergies in smart cities. To fully exploit the advantages of photovoltaic power generation and electric vehicles and to release the potential of electric vehicles as distributed energy storage facilities, this paper develops a multi-objective robust optimization framework that accounts for the benefits of multiple parties of smart charging and discharging systems and depicts a bounded uncertainty set based on partial statistical information from real data. The original model is scalarized and linearized using efficient methods such as max-ordering scalarization and the robust augmented weighted Tchebycheff to facilitate the solution. Moreover, a smart charging and discharging scheduling strategy based on a convergent demand response strategy is proposed to achieve better demand -side management. A case study, based on real data from Car2go and the solar radiation intensity in Portland, Oregon, shows that (1) The methodological framework effectively addresses the smart scheduling issue that considers the interests of multiple parties in complex systems with uncertainty, in contrast to conventional methods. (2) The designed convergent demand response strategy promotes photovoltaic power generation and charging demand synergy and achieves regulation effects such as peak shaving and valley filling. (3) The proposed method and strategy yield good results in multiple aspects, such as charging costs, load regulation, and clean energy utilization, while enhancing the economic and environmental benefits.

关键词： Smart charging and discharging multi-objective robust optimization Photovoltaic power generation Demand-side management Economic and environmental benefits

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