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6th International Conference on Green Intelligent Transportation System and Safety (GITSS)

作者： Zhang, Lanxia Qin, Yong Meng, Xuelei Wang, Li Zhu, Tao Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Beijing Jiaotong Univ Beijing Res Ctr Urban Traff Informat Sensing & Se Beijing 100044 Peoples R China Informat Technol Chinese Railway Beijing 100844 Peoples R China

Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multi objective optimal model of train operation adjustment, whose optimization objective is to reduce the train delay time and the numbers of delay train. Since the model is established as an NP complete problem, a multi-objective particle swarm optimization algorithm (MPSO) is proposed to solve the complex problem. Considering the strategy of dispatcher' preference, MPSO can get a set of Pareto solutions in the actual train operation adjustment problems. The actual experiment, taking Beijing-Shanghai highspeed railway as example, is conducted to validate the feasibility of the algorithm compared with the basic particle swarm optimization algorithm (PSO). Results demonstrate that the model can capture the characteristics of the practical dispatching problem. MPSO is efficient for train operation adjustment and provides better solutions than the traditional approaches. (C) 2016 Published by Elsevier Ltd.

关键词： Train operation adjustment multi-objective optimization problem MPSO

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multi-objective Genetic Algorithm for an automatic transmission gear shift map

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IFAC-PapersOnLine 2016年第3期49卷 123-128页

作者： Fofana, Adama Haas, Olivier Ersanilli, Vince Burnham, Keith Mahtani, Joe Woolley, Chris Vithanage, Karen Adama Fofana is with Coventry University United Kingdom Olivier Haas is with Coventry University United Kingdom Vince Ersanilli is with Coventry University United Kingdom Keith Burnham is with Coventry University United Kingdom Joe Mahtani is with Coventry University United Kingdom Chris Woolley is with SAIC MOTOR UK Technical Centre Limited Lowhill Lane BirminghamB31 2BQ United Kingdom Karen Vithanage is Director of KJV Consultancy United Kingdom

Reductions in engine emissions including carbon dioxide (CO2) can be achieved by analysing the Brake Specific Fuel consumption (BSFC) map and selecting a set of gear ratio and associated gear shift map which will move the engine operating point into the most efficient region. This paper formulates the determination of the most appropriate gear shift map for a Dual Clutch Transmission as a multi objective optimisation problem. A multi-objective genetic algorithm (MOGA) is complemented by a new repair mechanism to enforce acceptable driveability in the solutions produced as well as a new problem specific local search operator exploiting engineering knowledge to determine feasible gear shift maps that result in reduced emissions. The solutions are evaluated against standard as well as novel objective formulations that take into account CO2, driveability, gearbox durability and other practical implementation considerations. A correlation analysis, combined with Pareto analysis, is performed to identify and visualise related objectives that can be combined using a traditional weighted sum approach and those that should be handled separately using a Pareto front approach to facilitate the trade-offs identification and resolutions. The first set of objectives aim to reduce CO2, the second to maintain driveabil-ity, and the third to consider durability. The novel approach was used to identify trade-off solutions focusing on reducing CO2emission. These solutions were validated on a prototype vehicle on a rolling road using the New European Driving Cycle, confirming the expected CO2savings whilst conserving good driveability characteristics. © 2016

关键词： Carbon dioxide Automobile transmissions Durability Economic and social effects Engines Fuel consumption Genetic algorithms multiobjective optimization Automatic transmission Brake specific fuel consumption Dual clutch transmission Local search operators multi objective genetic algorithm New european driving cycles Reducing co2 emissions Weighted sum approaches

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Planning Inspection Paths through Evolutionary multi-objective optimization 16

Planning Inspection Paths through Evolutionary Multi-objecti...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ellefsen, Kai Olav Lepikson, Herman A. Albiez, Jan C. SENAI CIMATEC Brazilian Inst Robot Salvador BA Brazil SENAI CIMATEC SENAI Inst Innovat Salvador BA Brazil DFKI GmbH Robot Innovat Ctr Brazilian Inst Robot Bremen Germany

ISBN: (纸本)9781450342063

We propose a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. We therefore apply an evolutionary multi-objective optimization algorithm to the problem which generates a set of solutions exploring the different ways to balance energy usage and coverage degree. To our knowledge, this is the first work that exploits the power of multiobjective evolution to explore the set of inspection plans that result in the most energy-efficient coverage of structures. The performance of the optimizer is demonstrated on a model of a subsea oilfield installation a type of structure that has many occluded and hidden parts, and that therefore illustrates the need for a method accepting imperfectly covering solutions.

关键词： Inspection Planning multiobjective Evolution Autonomous Underwater Vehicle

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A multi objective FLND problem under uncertain environment using robust optimization: A heuristic approach and case study 12

A multi objective FLND problem under uncertain environment u...

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12th International Conference on Industrial Engineering (ICIE)

作者： Shishebori, Davood Yousefi-Babadi, Abolghasem Davoudi, Ashkan Yazd Univ Yazd Iran Univ Tehran Tehran Iran Mazandaran Univ Sci & Technol Mazamdaran Iran

ISBN: (纸本)9781509004584

In this paper, a robust multi objective mixed integer linear programming model is proposed for a robust facility location network design problem, which takes uncertain parameters (including demand and transfer costs) into the account. The proposed model simultaneously minimizes the total operating costs and maximizes the total coverage. Moreover, an investment budget constraint is raised on facility location and link construction. Therefore, this model determines the optimal locations of new facilities, link roads that should be constructed and also demand nodes covered by these facilities and link roads. Then, the proposed model is robust by suitable approaches. An efficient multi objective approach, based on Lp-metrics-is proposed and tested for several test problems. A practical case study, based on an integrated distribution system, is presented in detail to illustrate the application of the proposed mathematical model. Finally, a sensitivity analysis is done to provide an insight into the behavior of the proposed model in response to changes of key parameters of the problem.

关键词： Facility location Network design Coverage multi-objective optimization Heuristic method uncertain environment robust stochastic programming Lp-metrics method Case study distribution system

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optimization and Evaluation of Environmental Operations for Three Gorges Reservoir

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WATER RESOURCES MANAGEMENT 2016年第10期30卷 3553-3576页

作者： Hu, Ming Huang, Guo H. Sun, Wei Ding, Xiaowen Li, Yongping Fan, Bin Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Environm Aquat Chem Beijing 100085 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China North China Elect Power Univ SC Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Univ Regina Fac Engn & Appl Sci Environm Syst Engn Program Regina SK S4S 0A2 Canada Univ Regina Inst Energy Environm & Sustainable Commun Regina SK S4S 7H9 Canada

This study proposed a multi-objective environmental reservoir operation methodology (MOEROM) and applied the new methodology to the Three Gorges Reservoir (TGR) in China. The MOEROM directly incorporated the statistical water quality models in the objective functions to reflect the response relationships between reservoir operations and water quality in three tributaries (the Xiangxi River, the Daning River and the Xiaojing River). A real-coded genetic algorithm with self-adaptive crossover operators was employed to solve the environmental reservoir operation model. And four different MCDA approaches (AHP, TOPSIS, Fuzzy AHP, and Fuzzy TOPSIS) were proposed to evaluate the optimal operational schemes. The results showed that TOPSIS and AHP were more suitable for selecting the schemes than the others because of the larger fluctuations. To demonstrate the advantage of MOEROM, the environmental operation schemes were compared with traditional and practical operation schemes in 2013. The advantages of the environmental operation scheme over the others were that it would help reach a compromise between economic benefit, flood control and water quality improvement. In the future, more water quality indices, uncertainties in water environmental information, and more efficient algorithms would be further considered and researched.

关键词： multi-objective programming Environmental operation Genetic algorithm MCDA Three Gorges Reservoir

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Sensitivity analysis of pareto solution sets of multiobjective optimization for a batch cooling crystallization process 2

Sensitivity analysis of pareto solution sets of multiobjecti...

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2nd Indian Control Conference (ICC)

作者： Hemalatha, Kilari Rani, Kalipatnapu Yamuna Indian Inst Chem Technol CSIR Div Chem Engn Proc Dynam & Control Grp Hyderabad 500007 Telangana India Indian Inst Chem Technol CSIR AcSIR Hyderabad 500007 Telangana India

ISBN: (纸本)9781467379939

An increased interest in pharmaceutical crystallization has been observed in recent years where the product quality is affected by variations in product crystal size distribution, purity, and morphology of the crystals which in turn affect the downstream processes such as filtration, milling, and eventually, formulation. Therefore, it is necessary to ascertain the optimal operation of the process in terms of optimal temperature policies in order to achieve the desired product crystal size distribution. In this work, the optimization of an unseeded batch cooling crystallization of paracetamol within a multi-objective framework is studied. The weight mean size and coefficient of variation of final crystals are considered as objectives, which give rise to a set of optimal solutions known as Pareto-optimal solutions using non dominated sorting genetic algorithm. The present work focuses on evaluating the sensitivity of optimal solutions for induced disturbances and uncertainties in process model mismatch during practical applications through simulation and optimization. It has been observed that the selection of a cooling profile exerts a strong influence on final product crystal size distribution which reiterates the importance of optimization and control in batch cooling crystallization processes industrially at a large scale. Further, in this study, an initial attempt has been made to provide some directions regarding solution approaches to the sensitivity issues.

关键词： batch cooling crystallization multiobjective optimization crystal size distribution sensitivity

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MPSO-Based Model of Train Operation Adjustment

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Procedia Engineering 2016年 137卷 114-123页

作者： Lanxia Zhang Yong Qin Xuelei Meng Li Wang Tao Zhu School of traffic and transportation Beijing Jiaotong University Beijing 100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044 China Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing Jiaotong University Beijing 100044 China School of traffic and transportation Lanzhou Jiaotong University Lanzhou 730070 China Information Technology for Chinese Railway Beijing 100844 China

Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multi-objective optimal model of train operation adjustment, whose optimization objective is to reduce the train delay time and the numbers of delay train. Since the model is established as an NP complete problem, a multi-objective particle swarm optimization algorithm (MPSO) is proposed to solve the complex problem. Considering the strategy of dispatcher’ preference, MPSO can get a set of Pareto solutions in the actual train operation adjustment problems. The actual experiment, taking Beijing-Shanghai high-speed railway as example, is conducted to validate the feasibility of the algorithm compared with the basic particle swarm optimization algorithm (PSO). Results demonstrate that the model can capture the characteristics of the practical dispatching problem. MPSO is efficient for train operation adjustment and provides better solutions than the traditional approaches.

关键词： Train operation adjustment multi-objective optimization problem MPSO

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Bio-inspired algorithms applied to the multi-objective vehicle routing problem: Frameworks and applications

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3997-4009页

作者： Wang, Yuan He, Yong-Ming He, Lei Xing, Li-Ning College of Information System and Management National University of Defense Technology Changsha410073 China

The multi-objective vehicle routing problem (MOVRP) is an extension of the vehicle routing problem (VRP). MOVRP is a classic multi-objective optimization problem. In recent years, the MOVRP has been applied for increased problem scales and complexity levels. As a result, to obtain better solutions to the MOVRP, bio-inspired algorithms have been introduced into this domain. This article provides an analysis of the MOVRP framework and a review of the bio-inspired algorithm approaches designed to solve MOVRPs. We thereby identify the bio-inspired algorithms that can achieve better solutions for MOVRPs and can be successfully applied to real-life situations. Some heating practical research areas of MOVRP, which include green vehicle routing, healthcare logistics, school bus routing and food supply chain management, are discussed as well. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Ant colony optimization

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An Approach to Identify Six Sigma Robust Solutions of multi/Many-objective Engineering Design optimization Problems

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JOURNAL OF MECHANICAL DESIGN 2015年第5期137卷 051404页

作者： Ray, Tapabrata Asafuddoula, Md Singh, Hemant Kumar Alam, Khairul Univ New S Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors' previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

关键词： Robust design optimization Uncertainty analysis multiobjective optimization Design Robustness Design optimization Algorithms

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multi-objective Parameter Configuration of Machine Learning Algorithms using Model-Based optimization

Multi-Objective Parameter Configuration of Machine Learning ...

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IEEE Symposium Series on Computational Intelligence

作者： Daniel Horn Bernd Bischl TU Dortmund Computational Statistics 44227 Dortmund Germany LMU München Computational Statistics 80539 München Germany

ISBN: (纸本)9781509042418

The performance of many machine learning algorithms heavily depends on the setting of their respective hyper-parameters. Many different tuning approaches exist, from simple grid or random search approaches to evolutionary algorithms and Bayesian optimization. Often, these algorithms are used to optimize a single performance criterion. But in practical applications, a single criterion may not be sufficient to adequately characterize the behavior of the machine learning method under consideration and the Pareto front of multiple criteria has to be considered. We propose to use model-based multi-objective optimization to efficiently approximate such Pareto fronts. Furthermore, the parameter space of many machine learning algorithms not only consists of numeric, but also categorical, integer or even hierarchical parameters, which is a general characteristic of many algorithm configuration tasks. Such mixed and hierarchical parameter space structures will be created naturally when one simultaneously performs model selection over a whole class of different possible prediction algorithms. Instead of tuning each algorithm individually, our approach can be used to configure machine learning pipelines with such a hierarchical structure, and efficiently operates on the joint space of all considered algorithms, in a multi-objective setting. Our optimization method is readily available as part of the mlrMBO R package on Github. We compare its performance against the TunePareto R package and the regular Latin Hypercube Sampling. We use a pure numerical setting of SVM parameter tuning and a mixed, hierarchical setting in which we optimize over multiple model spaces at once.

关键词： Configuration Machine Learning

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