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Reliability-Based Robust Design optimization in Consideration of Manufacturing Tolerance by multi-objective evolutionary optimization with Repair Algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS 2021年第5期18卷

作者： Li, Gang Liu, Ye Zhao, Gang Zeng, Yan Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

There are inherently various uncertainties in practical engineering, and reliability-based design optimization (RBDO) and robust design optimization (RDO) are two well-established methodologies when considering the uncertainties. Naturally, reliability-based robust design optimization (RBRDO) is a methodology developed recently by combining RBDO and RDO, in which the tolerances of random design variables are always assumed as constants. However, the tolerance of random design variables is a key factor for the objective robustness and manufacturing cost, and the tolerance allocation is the core problem in mechanical manufacturing. Inspired by the cost-tolerance relationship in mechanical manufacturing, this paper presents an integrated framework to simultaneously find the optimal design variable and the corresponding tolerance in the multi-objective RBRDO, with the trade-off between objective robustness and manufacturing cost. The failure mechanism of the constraint handling strategy of the constrained reference vector-guided evolutionary algorithm (C-RVEA) is discussed to solve the multi-objective optimization formulation. Then the robust repair operator and reliability-based repair operator are proposed to transform unfeasible solutions to the feasible ones under reliability constraints. Numerical results reveal that the proposed repair algorithm is effective. By solving the integrated multi-objective optimization problem, the Pareto front with the compromised solutions between the objective robustness and manufacturing cost could be obtained, from which decision makers can select the satisfying solution conveniently according to the preferred requirements.

关键词： Tolerance allocation robust design optimization multi-objective evolutionary optimization Pareto optimality constraint handling strategy

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A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

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JOURNAL OF ADVANCED RESEARCH 2015年第3期6卷 417-423页

作者： Adly, Amr A. Abd-El-Hafiz, Salwa K. Cairo Univ Fac Engn Elect Power & Machines Dept Giza 12613 Egypt Cairo Univ Fac Engn Dept Engn Math Giza 12613 Egypt

Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper. (C) 2014 Production and hosting by Elsevier B.V. on behalf of Cairo University.

关键词： Power transformers Design multi-objective evolutionary optimization Particle swarm optimization

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multi-objective robust evolutionary optimization of the boring process of AISI 4130 steel

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第5-6期112卷 1745-1765页

作者： Vieira, Jessica Tito Pereira, Robson Bruno Dutra Freitas, Samuel Alves Lauro, Carlos Henrique Brandao, Lincoln Cardoso Fed Univ Sao Joao del Rei UFSJ Dept Mech Engn Ctr Innovat Sustainable Mfg CIMS Praca Frei Orlando 170 Sao Joao Del Rei MG Brazil

Boring is widely applied to enlarge holes. The high L/D ratio of boring bars enables self-excited vibration, deteriorating the quality of the hole. Therefore, this work aims the multi-objective evolutionary robust optimization of the boring process. Robust parameter design is employed to achieve robust models for roughness and roundness concerning tool overhang length and borehole depth, set as noise variables. These models aid the attainment of control factors' levels, i.e., feed, cutting speed, and fixture position, which turn the responses less sensitive to noise. The robust models together with the material removal rate deterministic model are optimized through evolutionary multi-objective methods. The effects of process and noise factors are discussed considering literature. The multi-objective evolutionary optimization of the robust models helps to achieve these robust levels of process factors besides balancing the trade-off between the outcomes. The multi-objective robust evolutionary results outperform the scalarization approach considered for comparison purposes.

关键词： Boring Internal turning Robust parameter design multi-objective evolutionary optimization

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A multi-objective robust evolutionary optimization approach applied to the multivariate helical milling process of super duplex steel

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APPLIED SOFT COMPUTING 2023年 147卷

作者： de Toledo, Jose Verissimo Ribeiro Pires, Thais Fernanda Pereira, Robson Bruno Dutra Ferreira, Joao Roberto Univ Fed Itajuba Inst Mech Engn Itajuba MG Brazil Univ Fed Itajuba Inst Ind Engn & Management Itajuba Brazil Univ Fed Sao Joao del Rei Ctr Inovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn Sao Joao Del Rei Brazil

This work presents an approach for multivariate modeling and multi-objective robust evolutionary optimization for processes presenting as inputs control and noise variables and as outputs a set of correlated responses regarding process performance and product quality. Factor analysis is applied to obtain a transformed output set that is uncorrelated and retains most part of the information of the original outputs. Models that are robust regarding noise variables relating this transformed output set with the control variables are obtained through robust parameter design. Response models in the function of process and noise variables are obtained through the least squares method. Mean and variance models as a function of process variables are obtained through error propagation. Robust mean square error models are obtained to model the bias and variance of each latent variable. Finally, multi-objective evolutionary optimization is performed. Three evolutionary algorithms were applied and compared through hypervolume. The approach is applied in the helical milling of super duplex stainless steel UNS S32760. Tool overhang length, hole depth, and lubri-cooling flow rate were set as noise factors. Cutting forces were monitored, while roughness and roundness were measured to evaluate process performance and product quality. The three factors, obtained to represent the six outputs, guaranteed dimensionality reduction and minimized the redundancy in output space. The mean square error models obtained to these three factors as a function of control variables minimize both bias and variance regarding noise factors. The AGE-MOEA multi-objective evolutionary algorithm obtained the best performance considering the hypervolume. Some solutions with high trade-off were selected through pseudo-weights to aid the decision-making.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Factor analysis Robust parameter design multi-objective evolutionary optimization Helical milling Super duplex stainless steel UNS S32760

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Improving evolutionary algorithm performance for integer type multi-objective building system design optimization

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ENERGY AND BUILDINGS 2016年 127卷 714-729页

作者： Xu, Weili Chong, Adrian Karaguzel, Omer T. Lam, Khee Poh Carnegie Mellon Univ Ctr Bldg Performance & Diagnost Pittsburgh PA 15213 USA

Building system design optimization is becoming popular for design decision making. State-of-the-art technique that couples evolutionary algorithms with a building simulation engine, which is time consuming and often cannot reach the "true" optimal solutions. Studies addressing these issues focus on implementing strategies such as fine tuning optimization algorithm's parameters, hybrid evolutionary algorithms with a local search algorithm or optimizing meta-models. Unlike the previous studies, this paper proposes two improvement strategies for building system design optimization. The two strategies, adaptive operators approach and adaptive meta-model approach, modify the behaviors of conventional evolutionary algorithms to improve the optimization convergency and speed performance. To demonstrate the effectiveness of these two strategies compared to conventional algorithms, a case study was conducted. The case study observed high convergency performance from both strategies with 30% and 60% time savings respectively. Furthermore, this study examines the performance comparison in respect to convergency, diversity preservation and speed between these two strategies. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary optimization Building system design Building cost estimation optimization performance

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Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 47卷 95-105页

作者： Domingo-Perez, Francisco Luis Lazaro-Galilea, Jose Wieser, Andreas Martin-Gorostiza, Ernesto Salido-Monzu, David de la Llana, Alvaro Univ Alcala Dept Elect E-28806 Alcala De Henares Spain ETH Inst Geodesy & Photogrammetry CH-8093 Zurich Switzerland

This paper focuses on the application of a decision support system based on evolutionary multi-objective optimization for deploying sensors in an indoor localization system. Our methods aim to provide the human expert who works as the sensor resource manager with a full set of Pareto efficient solutions of the sensor placement problem. In our analysis, we use five scalar performance measures as objective functions derived from the covariance matrix of the estimation, namely the trace, determinant, maximum eigenvalue, ratio of maximum and minimum eigenvalues, and the uncertainty in a given direction. We run the multi-objective genetic algorithm to optimize these objectives and obtain the Pareto fronts. The paper includes a detailed explanation of every aspect of the system and an application of the proposed decision support system to an indoor infrared positioning system. Final results show the different placement alternatives according to the objectives and the trade-off between different accuracy performance measures can be clearly seen. This approach contributes to the current state-of-the art in the fact that we point out the problems of optimizing a single accuracy measure and propose using a decision support system that provides the resource manager with a full overview of the set of Pareto efficient solutions considering several accuracy metrics. Since the manager will know all the Pareto optimal solutions before deciding the final sensor placement scheme, this method provides more information than dealing with a single function of the weighted objectives. Additionally, we are able to use this system to optimize objectives obtained from fairly complex functions. On the contrary, recent works that are referenced in this paper need to simplify the localization process to obtain tractable problem formulations. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Indoor positioning Sensor placement Infrared sensors multi-objective evolutionary optimization Decision support systems

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Communication efficiency optimization in federated learning based on multi-objective evolutionary algorithm

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evolutionary INTELLIGENCE 2023年第3期16卷 1033-1044页

作者： Chai, Zheng-yi Yang, Chuan-dong Li, Ya-lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

Federated learning is an emerging technology that can effectively safeguard personal information. As opposed to traditional centralized learning, federated learning can avoid data sharing while maintaining global model training. However, in the process of updating the global model, it will consume huge client communication resources, which hinder the wide application of this technology. To reduce the communication overhead without seriously reducing the accuracy of the global model, under the federated learning framework, we use decomposition based multi-objective optimization algorithm (MOEA/D) to optimize the structure of the global model. For the structure of the global model, a highly scalable coding method is used for coding, which improves the efficiency of the evolutionary neural network. As a comparison, we use the non-dominated sorting genetic algorithm II(NSGA II) to optimize the problem under the same conditions and verify the effectiveness of both algorithms according to the obtained Pareto solution. We verify that MOEA/D has better convergence when using multilayer and convolutional neural networks as the global model. Overall, MOEA/D can further strengthen the structure optimization of the federated learning model and reduce communication costs.

关键词： Federated learning multi-objective evolutionary optimization Communication cost Deep neural networks Network connectivity

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Non-linear identification of a Peltier cell model using evolutionary multi-objective optimization

Non-linear identification of a Peltier cell model using evol...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Huilcapi, Victor Herrero, Juan Manuel Blasco, Xavier Martinez-Iranzo, Miguel Univ Politecn Salesiana Fac Ingn Robles 107 Guayaquil Ecuador Univ Politecn Valencia Inst Univ Automat & Informat Ind Camino de Vera S-N Valencia 46022 Spain

In this paper a technique for identifying a first principles model of a Peltier cell using multi-objective evolutionary optimization and experimental data is proposed. The methodology of multi-objective optimization applied together with the method of representation of the Pareto front called level diagram enables the analysis of the set of models obtained for the temperatures of a Peltier cell in different scenarios. Level diagrams facilitate the selection of a compromise model to characterize the thermoelectric module successfully. The main advantage of the multi objective design methodology is to easily understand the trade-off among several Pareto optimal solutions. The designer can select the final solution according to his/her preferences without missing the knowledge available from the set of optimal solutions. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary optimization Level diagrams Identification Peltier cell Pareto front multivariable system modeling

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An Ethical multi-Stakeholder Recommender System Based on evolutionary multi-objective optimization 13

An Ethical Multi-Stakeholder Recommender System Based on Evo...

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13th IEEE International Conference on Services Computing (SCC) / 13th IEEE International Conference on Web Services (ICWS)

作者： Kermany, Naime Ranjbar Zhao, Weiliang Yang, Jian Wu, Jia Pizzato, Luiz Macquarie Univ N Ryde NSW Australia Donghua Univ Shanghai Peoples R China Commonwealth Bank Australia AI Labs Sydney NSW Australia

ISBN: (纸本)9781728187891

In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.

关键词： multi-stakeholder recommender systems long-tail recommendation multi-objective evolutionary optimization P-fairness diversity

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Non-linear identification of a Peltier cell model using evolutionary multi-objective optimization

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IFAC-PapersOnLine 2017年第1期50卷 4448-4453页

作者： Huilcapi V. Herrero J.M. Blasco X. Martínez-Iranzo M. Universidad Politécnica Salesiana Facultad de Ingenierías Robles 107 Guayaquil Ecuador Instituto Universitario de Automática e Informática Industrial Universitat Politécnica de Valencia Camino de Vera s/n Valencia 46022 Spain

In this paper a technique for identifying a first principles model of a Peltier cell using multi-objective evolutionary optimization and experimental data is proposed. The methodology of multi-objective optimization applied together with the method of representation of the Pareto front called level diagram enables the analysis of the set of models obtained for the temperatures of a Peltier cell in different scenarios. Level diagrams facilitate the selection of a compromise model to characterize the thermoelectric module successfully. The main advantage of the multi-objective design methodology is to easily understand the trade-off among several Pareto optimal solutions. The designer can select the final solution according to his/her preferences without missing the knowledge available from the set of optimal solutions. © 2017

关键词： Identification Level diagrams multi-objective evolutionary optimization multivariable system modeling Pareto front Peltier cell

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