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Conjunctive Management of Large-Scale Pressurized Water Distribution and Groundwater Systems in Semi-Arid Area with parallel genetic algorithm

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WATER RESOURCES MANAGEMENT 2009年第8期23卷 1497-1517页

作者： Tsai, Frank T. -C. Katiyar, Vineet Toy, Doug Goff, Robert A. Louisiana State Univ Dept Civil & Environm Engn Baton Rouge LA 70803 USA City Chandler Municipal Util Dept Water Resources Chandler AZ 85244 USA

This study develops a production well management model for the conjunctive management of water resources in semi-arid areas. The management model integrates a large-scale pressurized water distribution system and a three-dimensional groundwater model under an optimization framework. The well pump operations optimization problem is formulated as a Boolean integer nonlinear programming (BINLP) problem to optimize the periodic 24-h pump on/off operations over a 1-week operation horizon. The management model considers multiple objectives and is solved by a parallel genetic algorithm (PGA) to overcome the difficulty of solving the BINLP problem. The PGA significantly reduces computation time for a case study in Chandler, Arizona. The Chandler water distribution model is built based on EPANET, and the Chandler three-dimension groundwater model is developed using MODFLOW. The high performance computing (HPC) of the genetic algorithm makes it possible to obtain 24-h real-time operations in the 7-day forecast model. The tank reliability, resilience, and vulnerability (R-R-V) are evaluated to infer the system reliability. The Pareto curve provides compromise solutions between the two competing objectives of energy reduction and pressure violation reduction.

关键词： Conjunctive management Water distribution system parallel genetic algorithm Groundwater Multi-objective

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A network parallel genetic algorithm for the one machine sequencing problem

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 1999年第3期37卷 71-78页

作者： Mayer, MK Univ Virginia Dept Syst Engn Charlottesville VA 22903 USA

This paper presents a network parallel genetic algorithm for the one machine sequencing problem. It examines a parallel genetic algorithm in which processors exchange their best solution found at periodic intervals and the case when no exchange is performed. The network parallel genetic algorithm is executed on a cluster of IBM RS/6000 workstations using a master-slave approach. Performance to a serial genetic algorithm is reported. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： parallel genetic algorithm parallel performance sequencing problem parallel processing

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Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem

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SOFT COMPUTING 2014年第12期18卷 2385-2396页

作者： Lu, Hui Liu, Jing Niu, Ruiyao Zhu, Zheng Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications, despite the fact that it is an NP-complete problem. The main feature of TTSP is the close interactions between task sequence and the scheme choice. Based on this point, the parallel implantation of genetic algorithm, called parallel genetic algorithm (PGA), is proposed to determine the optimal solutions. Two branches-the tasks sequence and scheme choice run the classic genetic algorithm independently and they balance each other due to their interaction in the given problem. To match the frame of the PGA, a vector group encoding method is provided. In addition, the fitness distance coefficient (FDC) is first applied as the measurable step of landscape to analyze TTSP and guide the design of PGA when solving the TTSP. The FDC is the director of the search space of the TTSP, and the search space determinates the performance of PGA. The FDC analysis shows that the TTSP owes a large number of local optima. Strong space search ability is needed to solve TTSP better. To make PGA more suitable to solve TTSP, three crossover and four selection operations are adopted to find the best combination. The experiments show that due to the characteristic of TTSP and the randomness of the algorithm, the PGA has a low probability for optimizing the TTSP, but PGA with Nabel crossover and stochastic tournament selection performs best. The assumptions of FDC are consistent with the success rate of PGA when solving the TTSP.

关键词： Test task scheduling problem parallel genetic algorithm Fitness distance coefficient genetic operators

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Bio-based connections and hybrid planar truss: A parallel genetic algorithm approach for model updating

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COMPUTERS & STRUCTURES 2024年 301卷

作者： Shi, Da Marano, Giuseppe Carlo Demartino, Cristoforo Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Politecn Torino Dipartimento Ingn Strutturale Edile & Geotecn DISEG Corso Duca Abruzzi 24 I-10128 Turin Italy Zhejiang Univ Zhejiang Univ Univ Illinois Urbana Champaign Inst Haining 314400 Zhejiang Peoples R China Roma Tre Univ Dept Architecture I-00153 Rome Italy

Bolted steel to laminated bio-based material connections experience significant performance challenges due to the nonlinear response and high stress concentrations at their joints. This paper introduces an innovative 3D plasticity -fracture continuum Finite Element (FE) model that significantly advances the simulation of such truss joints by integrating Hill's yielding criteria with an element removal methodology for fracture simulation. This novel approach captures both plastic and fracture behaviors simultaneously, a capability not sufficiently addressed in existing models. We detail the theoretical framework for these models, including the derivation of constitutive equations and the algorithms necessary for their implementation in ABAQUS. Additionally, it is provided a low -fidelity modeling of truss joints, offering a comprehensive analysis of connector elements, joint models, and parametric modeling via Python scripting. The model's efficacy is demonstrated through identification of connection and of hybrid planar trusses under cyclic loading, which validates the practical applicability of the method. To optimize computational efficiency, we developed a parallel genetic algorithm (PGA) that integrates seamlessly with ABAQUS and Python to facilitate parameter calibration. This integration not only enhances the model's accuracy but also reduces computational load, making it feasible for complex engineering applications. Our findings illustrate a significant improvement in modeling accuracy and efficiency, establishing a robust methodology for analyzing truss joints in bio-based construction materials.

关键词： parallel genetic algorithm Bio-based connections High-fidelity FE model Low fidelity FE model Hybrid planar truss Multi-thread parallel analysis

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Variable selection method for quantitative trait analysis based on parallel genetic algorithm

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ANNALS OF HUMAN geneticS 2010年第1期74卷 88-96页

作者： Mukhopadhyay, Siuli George, Varghese Xu, Hongyan Med Coll Georgia Dept Biostat Augusta GA 30912 USA Indian Inst Technol Dept Math Bombay 400076 Maharashtra India

P>Selection of important genetic and environmental factors is of strong interest in quantitative trait analyses. In this study, we use parallel genetic algorithm (PGA) to identify genetic and environmental factors in genetic association studies of complex human diseases. Our method can take account of both multiple markers across the genome and environmental factors, and also can be used to do fine mapping based on the results of haplotype analysis to select the markers that are associated with the quantitative traits. Using both simulated and real examples, we show that PGA is able to choose the variables correctly and is also an easy-to-use variable selection tool.

关键词： QTL parallel genetic algorithm complex traits

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ON SOLVING ENERGY-DEPENDENT PARTITIONED REAL SYMMETRIC MATRIX EIGENVALUE PROBLEM BY A parallel genetic algorithm

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JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2008年第6期7卷 1103-1120页

作者： Sharma, Rahul Nandy, Subhajit Bhattacharyya, S. P. Indian Assoc Cultivat Sci Dept Phys Chem Kolkata 700032 India Andrews High HS Sch Kolkata 700031 India

An energy-dependent partitioning scheme is explored for extracting a small number of eigenvalues of a real symmetric matrix with the help of a serial as well as parallel genetic algorithm (GA). The proposed method is tested on two matrices (up to 2000 x 2000) with an increasing number of processors in a master- slave architecture. A comparison is made with the Jacobi-Davidson method in serial mode as implemented in the JDQZ-package. Different partition sizes are used. Traditionally used Lowdin's method is also tested in both serial and parallel modes. The advantages and disadvantages of the parallel GA-based method in solving the partitioned eigenvalue problem are analyzed.

关键词： Symmetric matrix eigenvalue problem parallel genetic algorithm partitioning techniques energy-dependent partitioning Lowdin's method

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Cloud computing-based parallel genetic algorithm for gene selection in cancer classification

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NEURAL COMPUTING & APPLICATIONS 2018年第5期30卷 1601-1610页

作者： Keco, Dino Subasi, Abdulhamit Kevric, Jasmin Int Burch Univ Fac Engn & Informat Technol Sarajevo 71000 Bosnia & Herceg Effat Univ Coll Engn Jeddah 21478 Saudi Arabia

Cancer classification is one of the main steps during patient healing process. This fact enforces modern clinical researchers to use advanced bioinformatics methods for cancer classification. Cancer classification is usually performed using gene expression data gained in microarray experiment and advanced machine learning methods. Microarray experiment generates huge amount of data, and its processing via machine learning methods represents a big challenge. In this study, two-step classification paradigm which merges genetic algorithm feature selection and machine learning classifiers is utilized. genetic algorithm is built in MapReduce programming spirit which makes this algorithm highly scalable for Hadoop cluster. In order to improve the performance of the proposed algorithm, it is extended into a parallel algorithm which process on microarray data in distributed manner using the Hadoop MapReduce framework. In this paper, the algorithm was tested on eleven GEMS data sets (9 tumors, 11 tumors, 14 tumors, brain tumor 1, lung cancer, brain tumor 2, leukemia 1, DLBCL, leukemia 2, SRBCT, and prostate tumor) and its accuracy reached 100% for less than 25 selected features. The proposed cloud computing-based MapReduce parallel genetic algorithm performed well on gene expression data. In addition, the scalability of the suggested algorithm is unlimited because of underlying Hadoop MapReduce platform. The presented results indicate that the proposed method can be effectively implemented for real-world microarray data in the cloud environment. In addition, the Hadoop MapReduce framework demonstrates substantial decrease in the computation time.

关键词： Cancer classification Gene expression Hadoop MapReduce parallel genetic algorithm

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Optimization of Adaptive Transit Signal Priority Using parallel genetic algorithm

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Tsinghua Science and Technology 2007年第2期12卷 131-140页

作者： Guangwei Zhou Albert Gan L. David Shen HDR Engineering Inc. Tampa Florida 33607 USA Lehman Center for Transportation Research Florida International University Miami Florida 33174 USA College of Engineering and Computing Florida International University Miami Florida 33174 USA

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority （TSP） strategy that applies the parallel genetic algorithm （PGA） to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSIM （VISual SIMulation） simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.

关键词： adaptive traffic signal control transit signal priority parallel genetic algorithm traffic simulation traffic delay

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parallel genetic algorithm based common spatial patterns selection on time-frequency decomposed EEG signals for motor imagery brain-computer interface

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part1期80卷 000-000页

作者： Luo, Tian-jian Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Fujian Normal Univ Digital Fujian Internet Thing Lab Environm Monitor Fuzhou 361005 Peoples R China Fujian Normal Univ Coll Math & Informat Fuzhou 350117 Peoples R China

Since the nonlinear and non-stationary characteristics of electroencephalogram (EEG) signals, motor imagery based brain-computer interface (MI-BCI) have problems of poor recognition accuracy and robustness across subjects and recording sessions. To address such problems, we extract common spatial pattern features on a detailed decomposition of time segments and frequency bands (TSFBCSP) on EEG signals, and propose a novel encoding approach of genetic algorithm (GA) for features selection. The proposed approach selects the most robust and discriminative CSP features to enhance the accuracy and robustness of EEG signals recognition. To improve the efficiency of TSFBCSP features selection, the parallel GA is implemented on a MapReduce frame-work (MRPGA). Comparative experiments have been done on two publicly available datasets (2a and 2b) from BCI competition IV. The excellent average recognition accuracy shows that the proposed TSFBCSP-GA/MRPGA has a promising candidate performance improvement on MI-BCI. Classification accuracies based on the optimal EEG segments and sub-bands have achieved 74.92 % and 81.04 % for the dataset 2a and 2b, respectively, and the faster classification accuracies of 74.54 % and 79.78 % has been achieved for the MRPGA architecture. Ablation study further proves the feasibility and effectiveness of the proposed algorithm, and discusses the parameters setting of applying it to actual MI-BCI applications.

关键词： Motor imagery Brain computer interface Common spatial pattern Time segments and frequency bands parallel genetic algorithm

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Validation of a parallel genetic algorithm for image reconstruction from projections

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2003年第3期63卷 356-359页

作者： Knoll, P Mirzaei, S Ludwig Boltzmann Inst Nucl Med & Endocrinol Dept Nucl Med Wilhelminenspital A-1171 Vienna Austria Ludwig Boltzmann Inst Nucl Med & Endocrinol PET Ctr Wilhelminenspital A-1171 Vienna Austria

The problem of accurate image reconstruction from projections has repeatedly arisen over the last decades in a large number of scientific, medical and technical fields. Reconstruction algorithms use data from electron microscopes to reconstruct molecular structures or X-ray projection data to compute medical images. Usually, the applied projection data are noisy and therefore iterative algorithms are used to solve numerically a number of equations. Theory and empirical results demonstrate that genetic algorithms (GA) can accurately solve a broad class of problems, especially if noisy input data are used. GA are based on the evolution of random tries by individuals, and therefore the time to find an appropriate solution is rather long. In this work, we use a parallel approach using JavaSpaces to speed up a genetic reconstruction algorithm. (C) 2003 Elsevier Science (USA). All rights reserved.

关键词： parallel genetic algorithm reconstruction image processing JavaSpaces

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