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GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

APPLIED SOFT COMPUTING 2019年 79卷 310-325页

作者： Harkat, H. Ruano, A. E. Ruano, M. G. Bennani, S. D. Univ Algarve Fac Sci & Technol Faro Portugal Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Fes Morocco Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Univ Coimbra CISUC Coimbra Portugal

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target's locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture. Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples' fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hyperbola signatures GPR Classification multi-objective genetic algorithm Mutual information Feature selection Neural networks High order statistics

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Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm

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JOURNAL OF POWER SOURCES 2014年 247卷 729-737页

作者： Brand, Jonathan Zhang, Zheming Agarwal, Ramesh K. Washington Univ Dept Mech Engn & Mat Sci St Louis MO USA

A simple but reasonably accurate battery model is required for simulating the performance of electrical systems that employ a battery for example an electric vehicle, as well as for investigating their potential as an energy storage device. In this paper, a relatively simple equivalent circuit based model is employed for modeling the performance of a battery. A computer code utilizing a multi-objective genetic algorithm is developed for the purpose of extracting the battery performance parameters. The code is applied to several existing industrial batteries as well as to two recently proposed high performance batteries which are currently in early research and development stage. The results demonstrate that with the optimally extracted performance parameters, the equivalent circuit based battery model can accurately predict the performance of various batteries of different sizes, capacities, and materials. Several test cases demonstrate that the multi-objective genetic algorithm can serve as a robust and reliable tool for extracting the battery performance parameters. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Battery performance Equivalent circuit model multi-objective genetic algorithm Parameter extraction

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A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 381-394页

作者： Nitisiri, Krisanarach Gen, Mitsuo Ohwada, Hayato Tokyo Univ Sci Grad Sch Sci & Engn Dept Ind Adm Noda Chiba Japan Fuzzy Log Syst Inst Fukuoka Fukuoka Japan

Railway system is a reliable and efficiency major public transportation. It is supported by many countries since it has a less environmental effect compared to another type of transportation. As the railway networks have become larger and more complex with increasing passenger demand, both aspects from the passenger satisfaction and operational cost need to be satisfied. This paper proposes a Parallel multi-objective Evolutionary algorithm with Hybrid Sampling Strategy and learning-based mutation to solve the railway train scheduling problem. Learning techniques have been coupled with a multi-objective genetic algorithm to guide the search for better solutions. In this paper, we incorporate a learning-based algorithm into a mutation process. The evaluation process is divided into sub-process and calculated by a parallel computational unit using GPU CUDA framework. Two sets of numerical experiments based on a small-scale case of Thailand ARL transit line and a larger case of BTS transit network are implemented to verify the effectiveness of the proposed approaches. The experimental results show the effectiveness of the proposed algorithm comparing to sequential CPU computational and two classical multi-objective evolutionary algorithms. With the same number of operating trains, the proposed algorithm can obtain schedule with less average waiting time and the time used for computational is significantly reduced.

关键词： Railway scheduling multi-objective genetic algorithm Parallel computation CUDA

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Application of response surface method and multi-objective genetic algorithm to configuration optimization of Shell-and-tube heat exchanger with fold helical baffles

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APPLIED THERMAL ENGINEERING 2018年 129卷 512-520页

作者： Wang, Simin Xiao, Juan Wang, Jiarui Jian, Guanping Wen, Jian Zhang, Zaoxiao Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

A kind of shell-and-tube heat exchangers with fold baffles was proposed to eliminate the triangular leakage zones betifeen adjacent baffles. An effective algorithm combing second-order polynomial response surface method and multi-objective genetic algorithm was adopted to study the effect of fold baffle configuration parameters on the performance of flow and heat transfer. The helical angle, overlapped degree and shell-side inlet velocity were chosen as design parameters, and the Nusselt number and shell-side pressure drop were considered as objective functions. The results show that both the Nusselt number and shell-side pressure drop increase with the decrease of helical angle and shell-side inlet velocity, and increase with the increasing overlapped degree. A set of Pareto-optimal points were obtained, and the optimization results illustrate a good agreement with CFD simulation data with the relative deviation less than 3%. And the empirical correlations of Nusselt number and friction coefficient were obtained based on response surface method, the helical angle and overlapped degree were fitted into empirical correlations as correction factors for the first time. It is found that the adjusted coefficient of determination of the Nusselt number and friction coefficient is 0.943 and 0.999, respectively, which illustrate the fitting is correct and reliable. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Shell-and-tube heat exchangers Fold helical baffles Triangular leakage zones Response surface method multi-objective genetic algorithm Empirical correlation

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An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第9-12期91卷 3145-3158页

作者： Luo, Guofu Wen, Xiaoyu Li, Hao Ming, Wuyi Xie, Guizhong Zhengzhou Univ Light Ind Mech & Elect Engn Inst 5 Dongfeng Rd Zhengzhou 450002 Henan Peoples R China

Process planning and scheduling are two major sub-systems in a modern manufacturing system. In traditional manufacturing system, they were regarded as the separate tasks to perform sequentially. However, considering their complementarity, integrating process planning and scheduling can further improve the performance of a manufacturing system. Meanwhile, the multiple objectives are needed to be considered during the realistic decision-making process in a manufacturing system. Based on the above requirements from the real manufacturing system, developing effective methods to deal with the multi-objective integrated process planning and scheduling (MOIPPS) problem becomes more and more important. Therefore, this research proposes a multi-objective genetic algorithm based on immune principle and external archive (MOGA-IE) to solve the MOIPPS problem. In MOGA-IE, the fast non-dominated sorting approach used in NSGA-II is utilized as the fitness assignment scheme and the immune principle is exploited to maintain the diversity of the population and prevent the premature condition. Moreover, the external archive is employed to store and maintain the Pareto solutions during the evolutionary process. Effective genetic operators are also designed for MOIPPS. To test the performance of the proposed algorithm, three different scale instances have been employed. And the proposed method is also compared with other previous algorithms in literature. The results show that the proposed algorithm has achieved good improvement and outperforms the other algorithms.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm Immune principle External archive

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A multi-objective genetic algorithm for determining efficient Risk-Based Inspection programs

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2015年 133卷 253-265页

作者： Moura, Marcio das Chagas Lins, Isis Didier Droguett, Enrique Lopez Soares, Rodrigo Ferreira Pascual, Rodrigo Univ Fed Pernambuco CEERMA Ctr Risk Anal Reliabil & Environm Modeling Recife PE Brazil Univ Maryland Dept Mech Engn Ctr Risk & Reliabil College Pk MD 20742 USA Petrobras SA Rio De Janeiro Brazil Pontificia Univ Catolica Chile Dept Min Engn Phys Asset Management Lab Santiago Chile

This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and multi-objective genetic algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which also comply with restrictions imposed by international standards and/or local government regulations. The proposed RBI+MOGA approach has the following advantages: (i) a user-defined risk target is not required;(ii) it is not necessary to estimate the consequences of failures;(iii) the inspection expenditures become more manageable, which allows assessing the impact of prevention investments on the risk level;(iv) the proposed framework directly provides, as part of the solution, the information on how the inspection budget should be efficiently spent. Then, genetic operators are tailored for solving this problem given the huge size of the search space. The ability of the proposed RBI+MOGA in providing efficient solutions is evaluated by means of two examples, one of them involving an oil and gas separator vessel subject to internal and external corrosion that cause thinning. The obtained results indicate that the proposed genetic operators significantly reduce the search space to be explored and RBI+MOGA is a valuable method to support decisions concerning the mechanical integrity of plant equipment. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Inspection programs Risk reduction Risk-Based Inspection multi-objective genetic algorithm

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Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with multi-objective genetic algorithm for Sediment Flux Prediction during Typhoons

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WATER RESOURCES MANAGEMENT 2021年第8期35卷 2387-2408页

作者： Jhong, Bing-Chen Fang, Hsi-Ting Huang, Cheng-Chia Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei 106335 Taiwan Taiwan Integrated Disaster Prevent Technol Engn C New Taipei 236654 Taiwan Natl Taipei Univ Business Ctr Gen Educ Taipei 100026 Taiwan

Effectively assessing crucial monitoring sites with suspended sediment concentration (SSC) is a vital challenge for achieving accurate prediction of sediment flux on sluice gates at a dam in a reservoir watershed. To address this issue, an assessment framework based on a core concept of Data-Information-Knowledge-Wisdom (DIKW) hierarchy is proposed in this study. First, for the reasonable training of the coupled method, a two-dimentional layer-averaged density current model, SRH2D, is applied to simulate reasonable SSC data. The limited SSC data at monitoring sites collected from the field and at dam face, inflow, and outflow discharges are collected for validation of a calibrated numerical model. Second, a well-known data-driven method, Support Vector Machine (SVM), is coupled with multi-objective genetic algorithm (MOGA) as a sediment-flux-prediction (SFP) model in the proposed framework to evaluate effective monitoring sites with SSC. An application in the Shih-Men Reservoir is implemented to demonstrate the contribution of the proposed investigation framework. The results indicate that the spatial turbidity current movement is reasonably simulated by the numerical model and appropriate as reliable data for the SFP model. The SSCs at measured points located on the lower level at dam face are significantly higher. Moreover, the results also show that the simulated SSC at the monitoring sites located near the inflow point and dam face are relatively useful for SFP. The analyzed results are concluded that the well-established observation equipment at the inflow point and near the dam is necessary for obtaining high-quality measured data, which has become a significant key issue on reservoir operation management (ROM). Also, the proposed framework is expected to be helpful to improve the benefit of ROM as reference for decision makers.

关键词： Sediment flux prediction Suspended sediment concentration Reservoir Support vector machine multi-objective genetic algorithm Typhoon

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Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron-multi-objective genetic algorithm

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BMC PLANT BIOLOGY 2022年第1期22卷 1-13页

作者： Gammoudi, Najet Nagaz, Kamel Ferchichi, Ali Arid Lands Inst IRA Arid & Oases Cropping Lab Medenine 4119 Tunisia Natl Inst Agron Tunis 43 Charles Nicolle Tunis 1082 Tunisia

Background Contamination-free culture is a prerequisite for the success of in vitro - based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over-sterilization is potentially detrimental to plant tissue. The recent rise of machine learning algorithms in plant tissue culture proposes an advanced interpretive tool for the combinational effect of influential factors for such in vitro - based steps. Results A multilayer perceptron (MLP) model of artificial neural network (ANN) was implemented with four inputs, three sterilizing chemicals at various concentrations and the immersion time, and two outputs, disinfection efficiency (DE) and negative disinfection effect (NDE), intending to assess twenty-seven disinfection procedures of Pistacia vera L. seeds. Mercury chloride (HgCl2;0.05-0.2%;5-15 min) appears the most effective with 100% DE, then hydrogen peroxide (H2O2;5.25-12.25%;10-30 min) with 66-100% DE, followed by 27-77% DE for sodium hypochlorite (NaOCl;0.54-1.26% w/v;10-30 min). Concurrently, NDE was detected, including chlorosis, hard embryo germination, embryo deformation, and browning tissue, namely, a low repercussion with NaOCl (0-14%), a moderate impact with H2O2 (6-46%), and pronounced damage with HgCl2 (22-100%). Developed ANN showed R values of 0.9658, 0.9653, 0.8937, and 0.9454 for training, validation, testing, and all sets, respectively, which revealed the uprightness of the model. Subsequently, the model was linked to multi-objective genetic algorithm (MOGA) which proposed an optimized combination of 0.56% NaOCl, 12.23% H2O2, and 0.068% HgCl2 for 5.022 min. The validation assay reflects the high utility and accuracy of the model with maximum DE (100%) and lower phytotoxicity (7.1%). Conclusion In one more case, machine learning algorithms emphasized their ability to resolve commonly encountered problems. The current successful implementation of MLP-MOGA inspires its application for more

关键词： In vitro culture Artificial neural network multi-objective genetic algorithm Optimization Disinfection Pistacia vera

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CFD modeling and optimization of a latent heat storage unit for running a solar assisted single effect Li-Br absorption chiller using multi-objective genetic algorithm

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SUSTAINABLE CITIES AND SOCIETY 2017年 34卷 321-334页

作者： Ghorbani, Babak Kowsary, Farshad Ebrahimi, Sasan Vijayaraghavan, Krishna Simon Fraser Univ Sch Mechatron Syst Engn 250-13450 102 Ave Surrey BC V3T 0A3 Canada Univ Tehran Mech Engn Dept Tehran Iran

In this paper, the feasibility of a solar absorption refrigeration system to be powered by a latent heat storage (LHS) unit is investigated for a representative building. A single effect absorption chiller, utilizing Li-Br and water as working fluids is thermodynamically simulated. Then, the simulation of the latent heat storage unit is performed by applying finite difference method and the results were validated by the researches in the literature. Then, the geometry of a phase change material (PCM) based LHS system was optimized using multi-objective genetic algorithm for simultaneously minimizing the charging time, and maximizing the discharging time. Since the paper considers conflicting objectives, a Pareto front is presented that can be used for obtaining the optimum geometry according to the environmental conditions and working hours of the absorption system. As an illustrative example, the designed heat storage system was shown to be able to drive the 72 kW generator of an absorption system, for at least 10 h of operation in the discharging mode with the absence of sunlight. Therefore, it is possible to run absorption chillers under low-load operation conditions using the solar energy if the appropriate storage unit, such as what is introduced here, is used.

关键词： Single effect absorption system Li-Br and water Latent heat storage multi-objective genetic algorithm Optimization

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A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期51卷 757-767页

作者： Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Shao, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.

关键词： Flexible job-shop scheduling multi-objective genetic algorithm Pareto-optimality Immune and entropy principle

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