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2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Zhenyu Song Yajiao Tang Xingqian Chen Shuangbao Song Shuangyu Song Shangce Gao Faculty of Engineering University of Toyama

ISBN: (纸本)9781538619797;9781538619780

Predicting the three-dimensional structure of a protein from its amino acid sequence is an important issue in the field of computational biology and bioinformatics. It remains as an unsolved problem and attract enormous researchers' interests. Different from most conventional methods, we model the protein structure prediction(PSP) problem as a multiobjective optimization problem. A three-objective energy function based on three physical terms is designed to evaluate a protein conformation. A multi-objective evolutionary strategy algorithm coupled with preference information is proposed in this study. The preference information is used in the survival criteria, focusing on the exploration of search process. The experimental results based on five proteins in PDB library demonstrate the effectiveness of proposed method. The analysis of Pareto fronts indicates that the preference information can make solutions diverse in genotypic space. Thus, the proposed method gives a new perspective for solving PSP problems.

关键词： Protein structure prediction multi-objective optimization multi-objective evolutionary algorithm preference information utility function

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Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2018年第4期140卷 041002-041002页

作者： Ma, He Li, Ziyang Tayarani, Mohammad Lu, Guoxiang Xu, Hongming Yao, Xin Univ Birmingham Sch Engn Edgbaston B15 2TT England Univ Birmingham Sch Comp Sci Edgbaston B15 2TT England Univ Birmingham Sch Comp Sci Nat Computat Grp Edgbaston B15 2TT England

Over the past 20 years, with the increase in the complexity of engines, and the combinatorial explosion of engine variables space, the engine calibration process has become more complex, costly, and time consuming. As a result, an efficient and economic approach is desired. For this purpose, many engine calibration methods are under development in original equipment manufacturers and universities. The state-of-the-art model-based steady-state design of experiments (DOE) technique is mature and is used widely. However, it is very difficult to further reduce the measurement time. Additionally, the increasingly high requirements of engine model accuracy and robust testing process with high data quality by high-quality testing facility also constrain the further development of model-based DOE engine calibration. This paper introduces a new computational intelligence approach to calibrate internal combustion engine without the need for an engine model. The strength Pareto evolutionary algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 gasoline direct injection (GDI) engine test bench, a SIMULINK real-time based embedded system was developed and implemented to engine electronic control unit (ECU) through rapid control prototyping (RCP) and external ECU bypass technology. Experimental validations prove that the developed engine calibration approach is capable of automatically finding the optimal engine variable set which can provide the best fuel consumption and particulate matter (PM) emissions, with good accuracy and high efficiency. The introduced engine calibration approach does not rely on either the engine model or the massive test bench experimental data. It has great potential to improve the engine calibration process for industries.

关键词： automated engine calibration computational intelligence multi-objective evolutionary algorithm rapid control prototyping simulation

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Soil Recycling Among Construction Sites by Optimizing Schedule and Costs for Earthmoving

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2017年第2期16卷 439-446页

作者： Choi, Hyemi Park, Min-Ho Jeong, Dae-Min Kim, Ju-Hyung Hanyang Univ Dept Sustainable Architectural Engn Seoul South Korea Hanyang Univ Dept Architectural Engn 222 Wangsimni Ro Seoul South Korea

Recycling uncontaminated excavated construction soil is beneficial because it reduces the costs to abandon excess soil or obtain refill soil from a distant location while alleviating environmental burdens. For this reason, various methods and techniques to support on-site soil reuse have been explored. However, in order to increase the reuse rate, excavated soil should be recycled among different construction sites as well. As a prerequisite for reusing excess soil in this context, the construction schedules, type of soil, trading volume, and incurred costs must be coordinated. In order to consider all of these aspects, earthmoving among construction sites needs to be planned by means of multi-objective optimization. This paper aims to present a practical solution supporting inter-site soil trade by introducing a non-dominated sorting algorithm-II (NSGA-II), a type of multi-objective evolutionary algorithm (MOEA). A description of the optimization procedure is provided, and computational results are presented to prove the effectiveness of the selected method.

关键词： excavated soil solid waste management multi-objective evolutionary algorithm non-dominated sorting algorithm-II

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A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 465卷 124-140页

作者： Yue, Wei Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xian Polytech Univ Sch Sci Xian 710071 Peoples R China

Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Portfolio selection Fuzzy variable Possibilistic moments Entropy multi-objective evolutionary algorithm

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Hypersonic Blunt-Body Shape Optimization for Reducing Drag and Heat Transfer

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2017年第1期31卷 48-55页

作者： Seager, Christopher Agarwal, Ramesh K. Washington Univ Dept Mech Engn & Mat Sci St Louis MO 63130 USA Washington Univ Dept Mech Engn & Mat Sci Engn St Louis MO 63130 USA

A large design concern for high-speed vehicles such as next-generation launch vehicles or reusable spacecraft is the drag and heat transfer experienced at hypersonic velocities. In this paper, the optimized shapes for both minimum drag and minimum peak heat flux for an axisymmetric blunt body are developed using computational-fluid-dynamics software in conjunction with a genetic algorithm. For flowfield calculations, the commercial flow solver ANSYS Fluent is employed to solve the unsteady compressible Reynolds-averaged Navier-Stokes equations in conjunction with the shear-stress transport k-omega turbulence model. The hypersonic body shape is optimized using a multi-objective genetic algorithm to minimize both the drag and heat transfer. The multi-objective genetic algorithm creates a Pareto-optimal front containing the optimized shapes for various relative objectives of minimized drag and heat transfer. The results show a significant decrease in both the drag and peak heat flux and exhibit the expected changes in the body profile. It should be noted that shape optimizations of a blunt body in hypersonic flow for reducing both drag and heat flux through use of a multi-objective genetic algorithm are reported in this paper for the first time in the literature. The proposed methodology will allow the simulation and optimization of more complex shapes of hypersonic vehicles.

关键词： Heat transfer Genetic algorithm (GA) drag shape optimization multi-objective evolutionary algorithm minimum drag blunt bodies hypersonic vehicles hypersonic speed hypersonic flow CONJUNCTION Heat flux

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Exploring multi-objective trade-offs in the design space of a waste heat recovery system

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APPLIED ENERGY 2017年 195卷 114-124页

作者： Mokhtar, Maizura Burns, Stephen Ross, Dave Hunt, Ian Edinburgh Napier Univ Sch Engn & Built Environm Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland Scotlands Rural Coll SRUC SRUC Res Div Roslin Inst Bldg Easter Bush EH25 9RG Midlothian Scotland

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system's parameters and their trade-offs, providing another source of information for prospective installations. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Waste heat recovery Optimisation multi-objective evolutionary algorithm Mutually exclusive objective functions

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multi-objective inverse scheduling optimization of single-machine shop system with uncertain due-dates and processing times

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2017年第1期20卷 371-390页

作者： Mou, Jianhui Gao, Liang Li, Xinyu Pan, Quanke Mu, Jiancai Shandong Univ Technol Sch Mech Engn Zibo 255000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huali Sci & Technol Coll Guangzhou 510000 Guangdong Peoples R China

Generally, ideal manufacturing system environments are assumed before determining effective scheduling. However, the original schedule is no longer optimal or even to be infeasible due to many uncertain events. This paper investigates a multi-objective inverse scheduling problem in single-machine shop system with due-dates and uncertain processing parameters. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing parameters are considered to be uncertain stochastic parameters. First, a comprehensive mathematical model for multi-objective single-machine inverse scheduling problem (MSMISP) is addressed. Second, an effective hybrid multi-objective evolutionary algorithm (HMNL) is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In HMNL, using an effective decimal system encoding scheme and genetic operators, the non-dominated sorting based on NSGA-II is adapted for the MSMISP. In addition, hybrid HMNL are proposed by incorporating an adaptive local search scheme into the well-known NSGA-II, where applies a separate local search process, total six strategies, to improve quality of solutions. Furthermore, an on-demand layered strategy is embedded into the elitism strategy to keep the population diversity. Afterwards, an external archive set is dynamically updated, where a non-dominated solution is selected to participate in the creation of the new population. Finally, 36 public problem instances with different scales and statistical performance comparisons are provided for the HMNL algorithm. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve MSMISP in inverse scheduling domain.

关键词： Single-machine shop system Inverse scheduling multi-objective evolutionary algorithm Local search method Uncertain processing parameter

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Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

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APPLIED SOFT COMPUTING 2017年 52卷 687-698页

作者： Rahmadi, Ridho Groot, Perry Heins, Marianne Knoop, Hans Heskes, Tom Univ Islam Indonesia Dept Informat Kabupaten Sleman Daerah Istimewa Indonesia Radboud Univ Nijmegen Inst Comp & Informat Sci Nijmegen Netherlands Radboud Univ Nijmegen Inst Comp & Informat Sci Artificial Intelligence Nijmegen Netherlands Radboud Univ Nijmegen Inst Comp & Informat Sci Data Sci Grp Nijmegen Netherlands Radboud Univ Nijmegen Med Ctr Expert Ctr Chron Fatigue Nijmegen Netherlands Netherlands Inst Hlth Serv Res Utrecht Netherlands

Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect common causes of the observed variables but the use of independence tests makes it less reliable. The score-based approach produces a result that is easier to interpret as it also measures the reliability of the inferred causal relationships, but it is unable to detect common confounders of the observed variables. A drawback of both score-based and constrained-based approaches is the inherent instability in structure estimation. With finite samples small changes in the data can lead to completely different optimal structures. The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Structure search is performed over structural equation models. Our approach uses exploratory search but allows incorporation of prior background knowledge. We validated our approach on one simulated data set, which we compare to the known ground truth, and two real-world data sets for chronic fatigue syndrome and attention deficit hyperactivity disorder, which we compare to earlier medical studies. The results on the simulated data set show significant improvement over alternative approaches and the results on the real-word data sets show consistency with the hypothesis driven models constructed by medical experts. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Causal modeling Structural equation model Stabilityselection multi-objective evolutionary algorithm NSGA II

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A multi-objective Routing Strategy for QoS and Energy Awareness in Software-Defined Networks

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IEEE COMMUNICATIONS LETTERS 2017年第11期21卷 2416-2419页

作者： Fernandez-Fernandez, Adriana Cervello-Pastor, Cristina Ochoa-Aday, Leonardo Univ Politecn Cataluna Dept Network Engn Castelldefels 08860 Spain

Energy consumption is a key concern in the deployment and operation of current data networks, for which software-defined networks (SDNs) have become a promising alternative. Although several works have been proposed to improve the energy efficiency, these techniques may lead to performance degradations when QoS requirements are neglected. Inspired by this problem, this letter introduces a new routing strategy, jointly considering QoS requirements and energy awareness in SDN with in-band control traffic. To that end, we present a complete formulation of the optimization problem and implement a multi-objective evolutionary algorithm. Simulation results validate the performance improvement on critical network parameters.

关键词： Software-defined networks energy-aware routing quality of service multi-objective evolutionary algorithm in-band control traffic

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On the constraint of passive RFID sensor tag design with MOEA/D-DE

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2017年第1期59卷 83-86页

作者： Song, Xiaotian Wang, Gang Yang, Chenwei He, Yuxing Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

For passive RFID sensor tag, high sensing sensitivity is achieved at the expense of read range reduction with a fixed transmitted power of RFID reader. To maximize sensing sensitivity and ensure satisfied communication performance of tag at meanwhile, the constraint between them can be defined as a multi-objective problem. The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is applied for the first time to provide a direct and effective solution to this constraint. For demonstration, an effective permittivity sensor and a temperature sensor are designed with MOEA/D-DE, respectively. Both the simulated and measured results show the feasibility of applying MOEA/D-DE to design high-sensitivity sensor tag with satisfied communication performance. (c) 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:83-86, 2017

关键词： RFID sensor sensing sensitivity read range multi-objective evolutionary algorithm

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