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Novel Interactive Preference-Based multiobjective evolutionary optimization for Bolt Supporting Networks

IEEE TRANSACTIONS ON evolutionary COMPUTATION 2020年第4期24卷 750-764页

作者： Guo, Yi-Nan Zhang, Xu Gong, Dun-Wei Zhang, Zhen Yang, Jian-Jian China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Chinese Acad Sci Shenyang Inst Automat State key Lab Robot Shenyang 110016 Peoples R China Dongfang Elect Co Ltd Yantai 264000 Peoples R China Xiangtan Univ Sch Informat Engn Xiangtan 411105 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

Previous methods of designing a bolt supporting network, which depend on engineering experiences, seek optimal bolt supporting schemes in terms of supporting quality. The supporting cost and time, however, have not been considered, which restricts their applications in real-world situations. We formulate the problem of designing a bolt supporting network as a three-objective optimization model by simultaneously considering such indicators as quality, economy, and efficiency. Especially, two surrogate models are constructed by support vector regression for roof-to-floor convergence and the two-sided displacement, respectively, so as to rapidly evaluate supporting quality during optimization. To solve the formulated model, a novel interactive preference-based multiobjective evolutionary algorithm is proposed. The highlight of generic methods which interactively articulate preferences is to systematically manage the regions of interest by three steps, that is, "partitioning-updating-tracking" in accordance with the cognition process of human. The preference regions of a decision-maker (DM) are first articulated and employed to narrow down the feasible objective space before the evolution in terms of nadir point, not the commonly used ideal point. Then, the DM's preferences are tracked by dynamically updating these preference regions based on satisfactory candidates during the evolution. Finally, individuals in the population are evaluated based on the preference regions. We apply the proposed model and algorithm to design the bolt supporting network of a practical roadway. The experimental results show that the proposed method can generate an optimal bolt supporting scheme with a good balance between supporting quality and the other demands, besides speeding up its convergence.

关键词： Fasteners optimization Rocks Stability analysis Tunneling Bolt supporting network interaction multiobjective evolutionary optimization preference surrogate model

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A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation

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Journal of Mountain Science 2020年第8期17卷 1860-1873页

作者： YAN Yan ZHANG Yu HU Wang GUO Xiao-jun MA Chao WANG Zi-ang ZHANG Qun Key Laboratory of High-Speed Railway Engineering MOE/School of Civil EngineeringSouthwest Jiao tong UniversityChengdu 610031China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Key Laboratory of Mountain Surface Process and Hazards/Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu 610041China School of Soil and Water Conservation Beijing Forestry UniversityBeijing 100083China Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention Chengdu 610081China

At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through *** relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall ***,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression *** overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration *** ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold ***,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space *** study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.

关键词： Debris flow Critical rainfall thresholds multiobjective evolutionary optimization Artificial neural network Pareto optimality

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Sensitivity analysis applied to decision making in multiobjective evolutionary optimization

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IEEE TRANSACTIONS ON MAGNETICS 2006年第4期42卷 1103-1106页

作者： Avila, SL Lisboa, AC Krähenbühl, L Carpes, WP Vasconcelos, JA Saldanha, RR Takahashi, RHC Univ Fed Santa Catarina BR-88040970 Florianopolis SC Brazil Ecole Cent Lyon F-69130 Ecully France Univ Fed Minas Gerais BR-31160030 Belo Horizonte MG Brazil

The result of a multiobjective evolutionary optimization is an efficient solution set surrounded by other candidate solution points. To choose a final solution, we can perform a sensitivity study. Applying this methodology, disturbances that occur in real-world design problems are not neglected. This paper presents an easy way to perform the sensitivity analysis directly from the data generated from a multiobjective stochastic optimization process. No additional function evaluation is required. As an example, we have solved some optimization problems concerning electromagnetic devices.

关键词： multiobjective evolutionary optimization sensitivity analysis

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Tactical Fleet Mix Computation Using multiobjective evolutionary optimization

Tactical Fleet Mix Computation Using Multiobjective Evolutio...

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2010 IEEE World Congress on Computational Intelligence

作者： Stuive, Leanne Wesolkowski, Slawo Ghanmi, Ahmed Univ Waterloo Waterloo ON N2L 3G1 Canada DRDC CORA Ottawa ON Canada

ISBN: (纸本)9781424481262

A multi-objective model for determining optimal fleet mixes for a set of non-concurrent tactical missions is studied. An implementation of the multiobjective evolutionary algorithm (MEA) NSGA-II is used to solve the problem, and it is compared to an exhaustive search of the fleet mix space as well as a solution set obtained by a Mixed Integer Non-Linear Program. The exhaustive and MEA searches are compared with respect to both accuracy and computational complexity. The use of an alternative genotypic representation is investigated;its slow convergence is used to demonstrate the importance of chromosome choice, aligning with past research on locality and redundancy in genotypic representations.

关键词： NSGA-II computational complexity computational complexity evolutionary computation exhaustive search fleet mix space genotypic representation integer programming military vehicles mixed integer nonlinear program multiobjective evolutionary optimization multiobjective model nonconcurrent tactical missions nonlinear programming tactical fleet mix computation

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Offline Data-Driven multiobjective optimization evolutionary Algorithm Based on Generative Adversarial Network

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2024年第2期28卷 293-306页

作者： Zhang, Yu Hu, Wang Yao, Wen Lian, Lixian Yen, Gary G. Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Chinese Acad Mil Sci Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Sichuan Univ Coll Mat Sci & Engn Chengdu 610065 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Usually, data-driven multiobjective optimization problems (DD-MOPs) are indirectly solved by evolutionary algorithms through the built surrogate model which is well-trained from sample data. However, in most DD-MOPs, only a few available data can be practicably collected from real engineering experiments due to the unaffordable cost and time. The key challenge in such a DD-MOP is to prevent the serious deterioration on the accuracy of the obtained approximate Pareto front. In this article, two novel strategies, critical fitness for evolutionary algorithms and data augmentation for a surrogate model, are complementarily imposed by a generative adversarial network (GAN) to tackle with the challenges in DD-MOPs. In the critical fitness strategy, a new critical fitness, composed of the critical score from the discriminator of GAN and the prediction value of the surrogate model, is proposed to improve the accuracy of the approximate Pareto front of a DD-MOP. In the data augmentation strategy, some new samples are synthetized by the generator of GAN to build a better-trained surrogate model. As a result, the GAN concurrently serves the critical fitness strategy and the data augmentation strategy as the roles of "killing two birds with one stone." The performance of the proposed algorithm for DD-MOPs was well-verified over 26 benchmark problems and successfully applied to discover new NdFeB materials.

关键词： optimization Data models Generative adversarial networks evolutionary computation Computational modeling Mathematical models Search problems Critical fitness data-driven optimization problem (DDOP) generative adversarial network (GAN) material optimization design multiobjective evolutionary optimization

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Detection and Localization of False Data Injection Attacks in Smart Grids Applying an Interpretable Fuzzy Genetic Machine Learning/Data Mining Approach

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ENERGIES 2025年第7期18卷 1568-1568页

作者： Gorzalczany, Marian B. Rudzinski, Filip Kielce Univ Technol Dept Elect & Comp Engn Al 1000 Lec PP 7 PL-25314 Kielce Poland

In this paper, we consider the problem of accurate, transparent, and interpretable detection, as well as the localization of false data injection attacks (FDIAs) in smart grids. In order to address that problem, we employ our knowledge discovery machine learning/data mining (ML/DM) approach-implemented as a collection of fuzzy rule-based classifiers (FR-BCs)-characterized by a genetically optimized accuracy-interpretability trade-off. Our approach uses our generalization (showing better performance) of the well-known SPEA2 method to carry out the genetic learning and multiobjective optimization process. The main contribution of this work is designing-using our approach-a collection of fast, accurate, and interpretable FR-BCs for FDIA detection and localization from the recently published FDIA data that describe various aspects of FDIAs in smart grids. Our approach generates FDIAs' detection and localization systems characterized by very high accuracy (97.8% and 99.5% for the IEEE 14-bus and 118-bus systems, respectively) and very high interpretability (on average, 4.6 and 3.8 simple fuzzy rules for earlier-mentioned systems, respectively, i.e., a few easy to comprehend fuzzy rules). The contribution of this paper also includes a comparative analysis of our approach and 12 alternative methods applied to the same FDIAs' data. This analysis shows that our approach totally outperforms the alternative approaches in terms of transparency and interpretability of FDIA detection and localization decisions while remaining competitive or superior in terms of the accuracy of generated decisions.

关键词： false data injection attacks (FDIAs) machine learning (ML) data mining (DM) fuzzy rule-based classifiers interpretable FDIA detection and localization multiobjective evolutionary optimization

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Online multiobjective evolutionary Approach for Navigation of Humanoid Robots

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2015年第9期62卷 5586-5597页

作者： Lee, Ki-Baek Myung, Hyun Kim, Jong-Hwan Kwangwoon Univ Coll Elect & Informat Engn Dept Elect Engn Seoul 139701 South Korea Korea Adv Inst Sci & Technol Coll Engn Dept Civil & Environm Engn Taejon 305338 South Korea Korea Adv Inst Sci & Technol Coll Informat Sci & Technol Dept Elect Engn Taejon 305338 South Korea

This paper proposes a novel online multiobjective evolutionary approach for the navigation of humanoid robots. In the proposed approach, the humanoid robot navigation problem is decomposed into a series of small multiobjective optimization problems (MOPs) with corresponding local information. Using multiobjective evolutionary algorithms (MOEAs), the MOPs can be successively solved while the robot is walking. In addition, to achieve significant reductions in the processing time of the MOEAs for online implementation while maintaining robustness and scalability, a novel homogeneous parallel computing method is devised for the MOEAs. multiobjective particle swarm optimization with preference-based sort (MOPSO-PS) is employed as the MOEA to reflect the user-defined preference for each objective during navigation. The effectiveness of the proposed online approach is demonstrated through well-known benchmark problems and a robot simulator. In both the simulation and the experiment, a humanoid robot successfully navigates to the goal, satisfying the preferences for various objectives, with local information in an environment without a global map.

关键词： Humanoid robot multiobjective evolutionary optimization navigation

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multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2019年第5期66卷 3825-3834页

作者： Jiang, Qingchao Gao, Furong Yan, Xuefeng Yi, Hui East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Clear Water Bay Hong Kong Peoples R China Nanjing Univ Technol Coll Elect Engn & Control Sci Nanjing 211816 Jiangsu Peoples R China

Successive batch processes generally involve within-batch and batch-to-batch correlations, and monitoring of such batch processes is imperative. This paper proposes a multiobjective two-dimensional canonical correlation analysis (M2D-CCA)-based fault detection scheme to achieve efficient monitoring of successive batch processes. First, three-way historical batch process data are unfolded into two-way time-slice data. Second, for each time-slice measurement, CCA is performed between the current measurement and previous measurements from both time and batch directions, which takes the within-batch and batch-to-batch correlations into account. To determine the involved measurements and eliminate the influence of unrelated variables, multiobjective evolutionary optimization is performed, which tries to maximize the preserved canonical correlation coefficients and minimize the number of involved variables. Finally, based on the established M2D-CCA model, an optimal fault detection residual is generated for each time-slice measurement. The M2D-CCA fault detection scheme performs fault detection using the current measurement and the information provided by its previous samples and batches, and therefore exhibits a superior monitoring performance. The M2D-CCA fault detection approach is tested on a numerical example and an industrial injection molding process. Monitoring results verify the feasibility and superiority of the proposed monitoring scheme.

关键词： Canonical correlation analysis (CCA) multiobjective evolutionary optimization multivariate statistical fault detection successive batch processes two-dimensional monitoring

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A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2007年第9期54卷 1667-1678页

作者： Fazendeiro, Paulo de Oliveira, Jose Valente Pedrycz, Witold Univ Beira Interior Dept Informat P-6200001 Covilha Portugal Univ Algarve UAlg Informat Lab P-8000117 Faro Portugal Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2E1 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

During surgeries (especially in long ones), patients are subject to a substantial amount of drug dosage necessary to achieve the required neuromuscular blockade level. This paper aims at the development of a fuzzy controller that satisfies two important goals: 1) an optimization of the amount of drug (atracurium) required to induce an adequate level of relaxation and 2) a concomitant ability to explain the undertaken control decision at the level of natural language. For instance, statements of the form '' Since the difference between the target and the current blockade level is near zero, a small quantity of drug infusion is currently being applied,'' where '' near zero '' and '' small '' are linguistic terms that are represented as fuzzy sets. In this sense, we can regard this controller as a construct that is human friendly and highly interpretable (transparent). To address the two objectives outlined above, we consider the use of a multiobjective evolutionary optimization. How the quality of the control action and the controller interpretability are formalized and captured in this optimization framework is presented. The effectiveness of the approach is demonstrated through a comprehensive suite of experiments involving 100 simulated patients (used for training) and 500 patients (forming the test set), validating the approach for application in the operating theater.

关键词： accuracy fuzzy controller interpretability multiobjective evolutionary optimization neuromuscular blockade semantic integrity

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On the use of multiobjective optimization for solving the Location Areas strategy with different paging procedures in a realistic mobile network

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APPLIED SOFT COMPUTING 2014年第0期18卷 146-157页

作者： Berrocal-Plaza, Victor Vega-Rodriguez, Miguel A. Sanchez-Perez, Juan M. Univ Extremadura Escuela Politecn Dept Techol Comp & Commun Caceres 10003 Spain

In this paper we research the location management, a vital task that controls the subscribers' mobility in the mobile communication networks. This management task defines a multiobjective optimization problem with two objective functions: minimize the number of location updates and minimize the paging overhead. In this work, the first objective function is described following the Location Areas strategy (because it is widely used in current mobile networks) and the second one is expressed following different paging procedures. Furthermore, the different location management strategies studied in this work are optimized with our versions of two well-known multiobjective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm II and the Strength Pareto evolutionary Algorithm 2). This study is a novel contribution of our research which will allow us to select the most suitable configuration of LA-PA (Location Areas with a specific paging procedure) depending on the real state of the signaling network. Experimental results also show that our proposals are very competitive because they outperform the results obtained by other optimization techniques proposed in the literature. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Mobile location management Location Areas scheme Paging procedure multiobjective evolutionary optimization Mobile network

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