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IEEE Circuits and Systems International Conference on Testing and Diagnosis

作者： Zhang, Yong Chen, Xixiang Liu, Guanjun Qiu, Jing Yang, Shuming Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424425860

A new approach to select an optimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The problem of optimal test points selection is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method efficiently and accurately rinds the optimal set of test points and is practical for large scale systems.

关键词： Test points selection system testing design for testability multi-objective genetic algorithm

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multi-objective fuzzy control of smart base isolated spatial structure

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2014年第3期14卷 547-556页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

Base isolation systems are used widely to reduce the dynamic responses of structures subjected to a seismic load. Recently, research has been conducted actively on smart base isolation systems that can effectively reduce the dynamic responses of isolated structures without accompanying increases in the base drifts. On the other hand, control performance of smart base isolation systems for spatial structures has not attracted significant attention. This study examined the dynamic response reduction capacity of a smart base isolation system for a spatial structure subjected to seismic excitation. MR dampers and low damping elastomeric bearings were used to compose a smart base isolation system, and its vibration control performance was compared with that of the optimally-designed, lead-rubber bearing (LRB) isolation system. A fuzzy controller was used to effectively control the spatial structure with a smart base isolation system. The dynamic responses of the spatial structure with an isolation system conflicted with the base drift. Therefore, these two responses were selected as the objective functions to apply a multi-objective genetic algorithm to optimize a fuzzy controller. The numerical simulation results showed that the smart base isolation system proposed in this study can reduce drastically the base drifts and seismic responses of the example spatial structure compared to the optimally designed LRB isolation system.

关键词： smart base isolation system spatial structure seismic response reduction fuzzy logic control multi-objective genetic algorithm MR damper lead-rubber bearing

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multi-objective Waste Load Allocation Model for Optimizing Waste Load Abatement and Inequality Among Waste Dischargers

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WATER AIR AND SOIL POLLUTION 2014年第3期225卷 1-17页

作者： Cho, Jae Heon Lee, Jong Ho Kwandong Univ Dept Hlth & Environm Kangnung 210701 Gangwon Do South Korea Cheongju Univ Dept Urban Planning Cheongju 360764 Chungbuk South Korea

In allocating the waste load of a river basin, the first priority is to achieve a given water quality goal for that river by utilizing several water quality management methods. Minimizing the waste load abatement cost within the river basin through appropriate, efficient water quality management is an important aspect of this process. In the past, it was common to concentrate on economic factors when constructing a waste load allocation (WLA) model. However, environmental resources (e. g., sub-basin area, population, wastewater flow, etc.) vary in each region of a river, and the fairness in the distribution of the treatment efforts among waste dischargersmust be considered. The WLA model in this study was constructed as a multi-objective optimization problem and was established to achieve the economic goal of minimizing waste load abatement and to consider the inequality among waste dischargers. Two types of inequality were introduced into the WLA model. The first type is the inequality in the waste load discharge regarding the environmental resources in each region was computed with the environmental resource-based Gini coefficient. The second type of inequality is the fairness in the distribution of the treatment efforts among waste dischargers. The suitability of this WLA model was verified with its application in a heavily polluted total maximum daily load subject river in South Korea. Furthermore, Pareto-optimal solutions drawn from the multi-objective genetic algorithm were analyzed to infer the least cost solution, the least inequality solution, and the compromise solutions and to verify critical pollution sources.

关键词： WLA Inequality ER-Gini multi-objective genetic algorithm Pareto-optimal solutions Critical pollution sources

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Disease Diagnosis with a hybrid method SVR using NSGA-II

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NEUROCOMPUTING 2014年 136卷 14-29页

作者： Zangooei, Mohammad Hossein Habibi, Jafar Alizadehsani, Roohallah Sharif Univ Technol Dept Comp Engn Tehran Iran

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to solve classification problems and vice versa. To regard a classification problem as a regression one, we propose a method based on the Support Vector Regression (SVR) classification model as one of the powerful methods in intelligent field management. We apply the Non-dominated Sorting genetic algorithm-II (NSGA-II), a kind of multi-objective evolutionary algorithm, to find mapping points (MPs) for rounding a real-value to an integer one. Also, we employ the NSGA-II to find out and tune the SVR kernel parameters optimally so as to enhance the performance of our model and achieve better results. The results of the study are compared with the results of some previous studies focusing on the diagnoses of four diseases using the same UCI machine learning database. The experimental results show that the proposed method yields a superior and competitive performance in these four real-world datasets. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Support Vector Regression multi-objective genetic algorithm Disease diagnosis Machine learning

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Adapting multireservoir Operation to Shifting Patterns of Water Supply and Demand

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WATER RESOURCES MANAGEMENT 2014年第3期28卷 625-643页

作者： Vonk, E. Xu, Y. P. Booij, M. J. Zhang, X. Augustijn, D. C. M. Univ Twente Dept Water Engn & Management NL-7522 NB Enschede Netherlands Zhejiang Univ Inst Hydrol & Water Resources Hangzhou 310058 Zhejiang Peoples R China

The aim of this study is to determine whether dam reoperation (the adjustment of reservoir operating rules) is an effective adaptation strategy to reduce the potential impacts of climate change and regional socio-economic developments. The Xinanjiang-Fuchunjiang reservoir cascade, located in Hangzhou Region (China), is selected as case study. We use a scenario-based approach to explore the effects of various likely degrees of water stress for the future period between 2011 and 2040, which are compared to the control period from 1971 to 2000. The scenario impacts are simulated with the WEAP water allocation model, which is interlinked with the NSGA-II metaheuristic algorithm in order to derive optimal operating rules adapted to each scenario. Reservoir performance is measured with the Shortage Index (SI) and Mean Annual Energy Production (MAEP). For the investigated scenarios, adapted operating rules on average reduce the SI with 84 % and increase the MAEP with 6.4 % (compared to the projected future performance of conventional operation). Based on the optimization results, we conclude that for the studied case dam reoperation is an effective adaptation strategy to reduce the impact of changing patterns of water supply and demand, even though it is insufficient to completely restore system performance to that of the control period.

关键词： Reservoir operation multi-objective genetic algorithm WEAP multireservoir system Climate adaptation Scenario analysis

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A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第5-8期75卷 755-770页

作者： Aghajani, Aydin Didehbani, Saeid Ahmadi Zadahmad, Manouchehr Seyedrezaei, Mir Hasan Mohsenian, Omid Islamic Azad Univ Ilkhchi Branch Dept Comp Engn Ilkhchi Iran Payame Noor Univ Dept Ind Engn Tehran Iran Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA

This paper presents a dynamic multi-objective mixed integer mathematical model for cell formation problem with probabilistic demand and machine reliability analysis, where the total system costs, machine underutilization cost, and maximum system failure rate over the planning time periods are to be minimized simultaneously. The total system cost objective function calculates machine operating, internal part production, intercellular material handling, and subcontracting costs. Since in this type of problems, objectives are in conflict with each other, so finding an ideal solution (a solution that satisfies all objectives simultaneously) is not possible. Therefore, this study uses the augmented epsilon-constraint method (to solve small size problems) and a nondominated sorting genetic algorithm (NSGAII) (to solve large size problems) to find the Pareto optimal frontier that decision makers can select her/his preferred solution. Numerical examples will be solved to demonstrate the efficiency of the proposed algorithm.

关键词： Group technology Dynamic probabilistic demand multi-objective mathematical model Pareto optimal Exponential distribution Machine reliability Machine availability Poisson distribution multi-objective genetic algorithm

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Optimal sensor placement for event detection and source identification in water distribution networks

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JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA 2014年第1期63卷 51-57页

作者： Liu, Shuming Auckenthaler, Pierre Tsinghua Univ Sch Environm Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Univ VEOLIA Environm Joint Res Ctr Adv E Beijing 100084 Peoples R China

This study focuses on the optimization of sensor placement with respect to the source identification and event detection. A multi-objective algorithm is used to solve the optimization problem. The numbers of possible source nodes for contamination events associated with the solutions on the Pareto fronts from the proposed method and benchmark method are calculated under the same configuration and compared. The comparison showed that the proposed method performs better than the benchmark method in detecting a contamination event and identifying its possible source.

关键词： multi-objective genetic algorithm nodal demand uncertainties sensor placement source identification

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multi-objective controller for enhancing nutrient removal and biogas production in wastewater treatment plants

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2014年第5期45卷 2537-2548页

作者： Kim, MinJeong Yoo, ChangKyoo Kyung Hee Univ Dept Environm Sci & Engn Coll Engn Yongin 446701 Gyeonggi Do South Korea

A multi-objective genetic algorithm (MOGA) aimed at solving a contradictory problem that occurs while combining several single-loop controllers into a multi-loop multi-objective controller (ML-MOC) was proposed to enhance the nutrient removal and biogas production in wastewater treatment plants at a low operational cost. First, an ML-MOC that consists of three proportional-integral (PI) controllers for improving nutrient removal and biogas production was developed. Then, a multi-objective optimization (MOO) was performed using the MOGA in order to determine the optimal set-points of the ML-MOC. The conflicting objective functions are (I) to minimize the effluent loads, (2) to minimize the operational cost and (3) to maximize the biogas production. The proposed ML-MOC was applied to benchmark simulation model no. 2 (BSM2) which is a benchmark system for wastewater treatment plants for evaluating the performances of the control strategies. The results of this study demonstrates that the ML-MOC showed a better nutrient removal performance than the reference controller in BSM2 maintaining economic operational costs, where both nutrient treatment removal rate and biogas production were increased by 3.9% and 3.6%, respectively. (C) 2014 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： multi-loop multi-objective controller Proportional-integral control multi-objective genetic algorithm Benchmark simulation model no. 2 Wastewater treatment plant

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A scheduling model for serial jobs on parallel machines with different preventive maintenance (PM)

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期70卷 1579-1589页

作者： Mirabedini, S. Nima Iranmanesh, Hossein Univ Tehran Dept Ind Engn Tehran Iran

In factories during production, preventive maintenance (PM) scheduling is an important problem in preventing and predicting the failure of machines, and most other critical tasks. In this paper, we present a new method of PM scheduling in two modes for more precise and better machine maintenance, as pieces must be replaced or be repaired. Because of the importance of this problem, we define multi-objective functions including makespan, PM cost, variance tardiness, and variance cost;we also consider multi-parallel series machines that perform multiple jobs on each machine and an aid, the analytic network process, to weight these objectives and their alternatives. PM scheduling is an NP-hard problem, so we use a dynamic genetic algorithm (GA) (the probability of mutation and crossover is changed through the main GA) to solve our algorithm and present another heuristic model (particle swarm optimization) algorithm against which to compare the GA's answer. At the end, a numerical example shows that the presented method is very useful in implementing and maintaining machines and devices.

关键词： Scheduling Reliability Preventive maintenance multi-objective genetic algorithm

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Scheduling of virtual manufacturing cells with outsourcing allowed

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2014年第12期27卷 1079-1089页

作者： Arkat, Jamal Ghahve, Homa Univ Kurdistan Dept Ind Engn Sanandaj Iran

This paper presents a bi-objective mixed integer programming model for operations scheduling in virtual manufacturing cells where outsourcing is allowed, and set-up times are considered to be sequence dependent. Two objective functions of the model are the minimisation of the maximum completion time (or makespan) and the minimisation of the total cost of inter and intra-plant transportation. Two multi-objective solution algorithms are then developed to solve the proposed model. The first algorithm is an epsilon-constraint method which can find the exact set of efficient solutions for small-size problems. Since the investigated problem is non-polynomial (NP) hard, exact algorithms cannot be used for large-scale real-world cases and, therefore, a bi-objective genetic algorithm (GA) is developed as the second algorithm. A numerical example is given to evaluate the effectiveness of the proposed model and solution algorithms. The results reveal the superiority of the proposed model over the base models and demonstrate that in comparison with the epsilon-constraint method, the proposed GA can obtain efficient solutions in much less computational time.

关键词： virtual manufacturing cell scheduling outsourcing multi-objective genetic algorithm epsilon-constraint method

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