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Force-based optimization of pseudopotentials for non-equilibrium configurations

COMPUTER PHYSICS COMMUNICATIONS 2016年 201卷 106-118页

作者： Brock, Casey N. Paikoff, Brandon C. Sallih, Muhammad I. Md Tackett, Alan R. Walker, D. Greg Vanderbilt Univ Mat Sci Program 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Mech Engn 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Adv Comp Ctr Res & Educ 221 Kirkland Hall Nashville TN 37235 USA

We have used a multi-objective genetic algorithm to optimize pseudopotentials for force accuracy and computational efficiency. Force accuracy is determined by comparing interatomic forces generated using the pseudopotentials and forces generated using the full-potential linearized augmented-plane wave method. This force-based optimization approach is motivated by applications where interatomic forces are important, including material interfaces, crystal defects, and molecular dynamics. Our method generates Pareto sets of optimized pseudopotentials containing various compromises between accuracy and efficiency. We have tested our method for LiF, Si0.5Ge0.5, and Mo and compared the performance of our pseudopotentials with pseudopotentials available from the ABINIT library. We show that the optimization can generate pseudopotentials with comparable accuracy (in terms of force matching and equation of state) to pseudopotentials in the literature while sometimes significantly improving computational efficiency. For example, we generated pseudopotentials for one system tested that reduced computational work by 71% without loss of accuracy. These results suggest our method can be used to generate pseudopotentials on demand that are tuned for a user's specific application, affording gains in computational efficiency. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Pseudopotential DFT multi-objective genetic algorithm

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A New multi-objective genetic algorithm for Feature Subset Selection in Fatigue Fracture Image Identification

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JOURNAL OF COMPUTERS 2010年第7期5卷 1105-1111页

作者： Li Ling Li Ming Lu YuMing Zhang YongLiang Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Jiangsu Peoples R China Nanchang Hangkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China

Feature subset selection is the most important and difficult task in the field of fatigue fracture image identification. In this paper, a new method which is hybrid of linear prediction, called LP-Based multi-objective genetic algorithms (LP-MOGA) is proposed for fatigue fracture feature subset selection. In LP-MOGA, predicted new solutions with elite solutions by liner prediction to improve the local search ability. For fatigue fracture identification, texture character and fractal dimension feature are extracted for original features;and then, feature subset selection is performed by LP-MOGA, in which, the objective functions minimize error identification rate, undetected identification rate and selected featured number;at last, the identification is executed by quadratic distance classifier. Compared with other methods, the experiment results of actual data demonstrate the presented algorithm is effective.

关键词： multi-objective genetic algorithm Liner Prediction Feature Extraction Feature Subset Selection Fatigue Fracture Identification

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multi-objective optimization of hybrid backorder inventory model

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 51卷 76-84页

作者： Srivastav, Achin Agrawal, Sunil Pandit Dwarka Prasad Mishra Indian Inst Informat Jabalpur MP India Pandit Dwarka Prasad Mishra Indian Inst Informat Dept Mech Engn Jabalpur Dumna Airport RdPO Khamaria Jabalpur MP India Flat E-801 Jaipur 302001 Rajasthan India

This paper addresses inventory problem for the products that are sold in monopolistic and captive markets experiencing hybrid backorder (i.e., fixed backorder and time-weighted backorder). The problem with stochastic demand is studied first by developing single objective (cost) inventory model. Computational results of a numerical problem show the effectiveness of hybrid backorder inventory model over fixed backorder inventory model. The model is later extended to multi-objective inventory model. Three objectives of multi-objective inventory model are the minimization of total cost, minimization of stockout units and minimization of the frequency of stockout. A multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the inventory model and generate Pareto curves. The Pareto curves obtained for hybrid backorder inventory model are compared with the existing Pareto curves that are based on fixed backorder. The results show a substantial reduction in stockout units and frequency of stockout with a marginal rise in cost with proposed hybrid backorder inventory system in comparison to existing fixed backorder inventory system. Sensitivity analysis is done to study the robustness of total cost, order quantity, and safety stock factor with the change in holding cost. In the end, the performance of the MOPSO algorithm is compared with the multi-objective genetic algorithm (MOGA). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Backorder Inventory multi-objective particle swarm optimization Pareto multi-objective genetic algorithm

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Dynamic Scheduling of Virtual Cellular Based on Rolling Window under New Tasks

Dynamic Scheduling of Virtual Cellular Based on Rolling Wind...

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2017 the 2nd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2017)

作者： Wenmin Han Yi Yu Ting Chen Zhengyi Li School of Economics and Management Jiangsu University of Science and Technology

ISBN: (纸本)9781510840683

Aiming at the dynamic scheduling problem of virtual cellular generated by the random arrival of new tasks,combined with the rolling window technology,the decision-making judgment based on the order completion trigger and the machine idle state trigger is put *** the same time,the dynamic random scheduling period is divided into continuous interval of static *** a non-linear multi-objective 0-1 integer programming model is proposed,which is based on the maximum completion time,the weighted total delay and the initial scheduling degree of deviation as the *** multi-objective genetic algorithm is used to solve the ***,taking the shipbuilding as an example,the feasibility and effectiveness of the rescheduling model are verified.

关键词： dynamic scheduling of virtual cellular rolling window multi-objective genetic algorithm

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Automated extraction of extended structured motifs using multi-objective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 2421-2426页

作者： Kaya, Mehmet Firat Univ Dept Comp Engn TR-23169 Elazig Turkey

A structured motif is defined as a collection of highly conserved simple motifs with pre-specified sizes and gaps between them. In structured motif extraction, while all simple motifs are unknown, all gap ranges are known earlier. In this paper, we propose a novel method using multi-objective evolutionary algorithm to extract automatically extended structured motifs in which all simple motifs and gap ranges are unknown. The method employs three conflicting objectives;similarity and support maximization and total gap range minimization. To the best of our knowledge, this is the first effort in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. We compare our method with the two well-known structured motif extraction methods, EXMOTIF and RISOTTO. Experiments conducted on synthetics and real data set demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime and accuracy. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Motif discovery Data mining multi-objective genetic algorithm

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Fuzzy force learning controller of flexible wiper system

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NEURAL COMPUTING & APPLICATIONS 2016年第2期27卷 483-493页

作者： Zolfagharian, Ali Valipour, P. Ghasemi, S. E. Univ Teknol Malaysia Dept Syst Dynam & Control Johor Baharu Johor Malaysia Islamic Azad Univ Dept Text & Apparel Qaemshahr Branch Qaemshahr Iran Islamic Azad Univ Young Reseachers & Elite Club Qaemshahr Branch Qaemshahr Iran

Wiper blade of automobile is among those types of flexible system that is required to be operated in quite high velocity to be efficient in high load conditions. This causes some annoying noise and deteriorated vision for occupants. The modeling and control of vibration and low-frequency noise of an automobile wiper blade using soft computing techniques are focused in this study. The flexible vibration and noise model of wiper system are estimated using artificial intelligence system identification approach. A PD-type fuzzy logic controller and a PI-type fuzzy logic controller are combined in cascade with active force control (AFC)-based iterative learning (IL). A multiobjective genetic algorithm is also used to determine the scaling factors of the inputs and outputs of the PID-FLC as well as AFC-based IL gains. The results from the proposed controller namely fuzzy force learning (FFL) are compared with those of a conventional lead-lag-type controller and the wiper bang-bang input. Designing controllers based on classical methods could become tedious, especially for systems with high-order model. In contrast, FFL controller design requires only tuning of some scaling factors in the control loop and hence is much simpler and efficient than classical design methods.

关键词： Automotive wiper System identification Intelligent control multi-objective genetic algorithm

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multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2016年第1期24卷 83-93页

作者： Besharati, S. R. Dabbagh, V. Amini, H. Sarhan, Ahmed A. D. Akbari, J. Hamdi, M. Ong, Z. C. Univ Malaya Fac Engn Dept Mech Engn Ctr Adv Mfg & Mat Proc Kuala Lumpur 50603 Malaysia Assiut Univ Fac Engn Dept Mech Engn Assiut Egypt Sharif Univ Technol Sch Mech Engn Tehran Iran

In this investigation, the multi-objective selection and optimization of a gantry machine tool is achieved by analytic hierarchy process, multi-objective genetic algorithm, and Pareto-Edgeworth-Grierson-multi-criteria decision-making method. The objectives include maximum static deformation, the first four natural frequencies, mass, and fabrication cost of the gantry. Further structural optimization of the best configuration was accomplished using multi-objective genetic algorithm to improve all objectives except cost. The result of sensitivity analysis reveals the major contribution of columns of gantry with respect to the crossbeam's contribution. After determining the most effective geometrical parameters using sensitivity analysis, multi-objective genetic algorithm was performed to obtain the Pareto-optimal solutions. In order to choose the final configuration, Pareto-Edgeworth-Grierson-multi-criteria decision-making was applied. The procedure outlined in this article could be used for selection and optimization of gantry as quantitative method as opposed to traditional qualitative method exploited in industrial application for design of gantry.

关键词： gantry machine tool structural optimization multi-criteria decision-making multi-objective genetic algorithm Pareto-Edgeworth-Grierson-multi-criteria decision-making

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Analysis and Research of Mooring System

Analysis and Research of Mooring System

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Jiahui Fan School of environment North China Electric Power University

ISBN: (纸本)9781510845008

In this paper, the original mooring system is optimized and improved under the condition of considering the water flow and water depth. The whole system is divided into a buoy system and a mooring system. For the buoy system, the moment of gravity of the buoy at the mean sea level is analyzed. For mooring system, the anchor cable will appear elastic deformation under the action of water flow force. So the particle spring model is constructed to analyze the force of the catenary segment. Then the new wind speed critical value is obtained. Thus, the state of existence of the chain is determined. A general optimization index is set up. And the optimal solution of the optimization index is calculated according to the multi-objective genetic algorithm. Thus the design of mooring system is completed.

关键词： Mooring system analytic hierarchy process multi-objective genetic algorithm

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PVM-based intelligent predictive control of HVAC systems

PVM-based intelligent predictive control of HVAC systems

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4th IFAC Conference on Intelligent Control and Automation Sciences (ICONS)

作者： Ruano, A. Pesteh, S. Silva, S. Duarte, H. Mestre, G. Ferreira, P. M. Khosravani, H. Horta, R. Univ Algarve Fac Sci & Technol Faro Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Univ Algarve Ctr Empresarial Gambelas EasySensing Intelligent Syst P-8005139 Faro Portugal Univ Lisbon Fac Ciencias LaSIGE P-1699 Lisbon Portugal Rolear SA P-8001906 Faro Portugal

This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in terms of economical savings and thermal comfort, are compared with standard, temperature regulated control.(1) (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： HVAC Model-Based Predictive Control Predicted Mean Vote Neural Networks multi-objective genetic algorithm Weather Station Wireless Sensor Networks

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multi-objective optimization of lithium-ion battery model using genetic algorithm approach

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JOURNAL OF POWER SOURCES 2014年 270卷 367-378页

作者： Zhang, Liqiang Wang, Lixin Hinds, Gareth Lyu, Chao Zheng, Jun Li, Junfu Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Natl Phys Lab Teddington TW11 0LW Middx England

A multi-objective parameter identification method for modeling of Li-ion battery performance is presented. Terminal voltage and surface temperature curves at 15 degrees C and 30 degrees C are used as four identification objectives. The Pareto fronts of two types of Li-ion battery are obtained using the modified multi-objective genetic algorithm NSGA-II and the final identification results are selected using the multiple criteria decision making method TOPSIS. The simulated data using the final identification results are in good agreement with experimental data under a range of operating conditions. The validation results demonstrate that the modified NSGA-II and TOPSIS algorithms can be used as robust and reliable tools for identifying parameters of multi-physics models for many types of Li-ion batteries. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Parameter identification multi-objective genetic algorithm multi-physics model Lithium-ion battery

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