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The parallel genetic algorithm for electromagnetic inverse scattering of a conductor

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2002年第5期79卷 573-586页

作者： Fan, Y Jiang, T Evans, DJ Nottingham Trent Univ Dept Comp Nottingham NG1 4BU England Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

A parallel genetic algorithm is presented to solve the inverse scattering problem which is formulated as an optimal problem where the cost function to be minimized is the energy norm of the residual, i.e. the difference between the estimated and observed field, and the parameters are the unknown control points for using a spline curve to construct the shape of the object conductor. This approach is computationally heavy since the direct problem needs to be solved in every optimization iteration in order to compute an estimated field. Experiments demonstrate our PGA provides an efficient method for such a problem.

关键词： inverse scattering parallel genetic algorithm moment method and cubic spline

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Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing

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INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE 2005年第15期45卷 1726-1734页

作者： Wang, ZG Rahman, M Wong, YS Sun, J Natl Univ Singapore Dept Mech Engn Singapore 119260 Singapore

This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： milling genetic algorithm simulated annealing parallel genetic algorithm

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An adaptive parallel genetic algorithm system for i-computing environment

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2003年第6期15卷 581-606页

作者： Wong, YY Lee, KH Leung, KS City Univ Hong Kong Comp Serv Ctr Kowloon Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Sha Tin Hong Kong Peoples R China

Many real-world optimization problems in the scientific and engineering fields can be solved by genetic algorithms (GAs) but it still requires a long execution time for complex problems. At the same time, there are many under-utilized workstations on the Internet. In this paper, we present a self-adaptive parallel GA system named APGAIN, which utilizes the spare power of the heterogeneous workstations on the Internet to solve complex optimization problems. In order to maintain a balance between exploitation and exploration, we have devised a novel probabilistic rule-driven adaptive model (PRDAM) to adapt the GA parameters automatically. APGAIN is implemented on an Internet Computing system called DJM. In the implementation, we discover that DJM's original load balancing strategy is insufficient. Hence the strategy is extended with the job migration capability. The performance of the system. is evaluated by solving the traveling salesman problem with data from a public database. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： internet computing distributed computing parallel genetic algorithm adaptive genetic algorithm

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Characterization of reservoir heterogeneity using inverse model equipped with parallel genetic algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2007年第9期29卷 823-838页

作者： Kwon, S. I. Sung, W. M. Huh, D. G. Lee, W. S. Hanyang Univ Dept Geoenvironm Syst Engn Seoul 133791 South Korea Korea Natl Oil Corp Anyang South Korea Korea Inst Geosci & Mineral Resources Taejon South Korea

This study presents the development of reservoir characterization inverse model equipped with ANN type of ISGA and MSGA parallel processing algorithms. In order to run the developed model efficiently, homogeneous PC-cluster was constructed by four connecting PCs that have the same features. The model adapted ANN to automatically determine optimum GA parameters of operator, number of individuals and the operation rate that is appropriate for the reservoir heterogeneity. By utilizing the developed model in this study, inverse calculation was conducted for the synthetic reservoir system with the aid of an ISGA-PP. Asaresult, it was found that convergence is stably progressed. In the result of permeability distribution, it shows that low permeable zone in the central area for the system studied appeared to be little different compared to the result obtained by Kriging method, which is used only as static data. In the matching result of pressure, maximum relative error of 1.54% was presented at OP-4, and hence, the calculated permeability distribution is thought to be quite reliable. When MSGA-PP was applied to thesame reservoir system as ISGA-PP, it converged stably similar to ISGA-PP. The difference between ISGA-PP and MSGA-PP appeared only at convergence rate and there sulting permeability distribution is very similar to each other. In the evaluation of computing efficiency of ISGA-PP and MSGA-PP against GA-SP, the result shows that the efficiency of parallel processing system is greater as the number of individual increases. Also, regardless of the number of individuals, the calculating time in parallel processing system was greatly reduced by 3.6 times compared to serial processing system of GA-SP. Finally, inverse calculation was carried out with MSGA-PP-ANN. As a result, it converged much faster than MSGA-PP, which does not have an artificial neural network system. This is the reason why the superior individuals are selected by the optimum operators, which are det

关键词： artificial neural network inverse model parallel genetic algorithm PC-cluster reservoir characterization

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Feature selection using cloud-based parallel genetic algorithm for intrusion detection data classification

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NEURAL COMPUTING & APPLICATIONS 2021年第18期33卷 11861-11873页

作者： Mehanovic, Dzelila Keco, Dino Kevric, Jasmin Jukic, Samed Miljkovic, Adnan Masetic, Zerina Int Burch Univ Fac Engn & Nat Sci Sarajevo 71000 Bosnia & Herceg

With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods;however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods.

关键词： parallel genetic algorithm Machine learning Feature selection Intrusion detection systems

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Matched-field inversion of sound speed profile in shallow water using a parallel genetic algorithm

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Chinese Journal of Oceanology and Limnology 2010年第5期28卷 1080-1085页

作者：余炎欣李整林何利 National Laboratory of Acoustics Institute of AcousticsChinese Academy of Sciences Haikou Laboratory of Acoustics Institute of AcousticsChinese Academy of Sciences Graduate School Chinese Academy of Sciences

A sound speed profile plays an important role in shallow water sound *** with in-situ measurements,many inversion methods,such as matched-field inversion,have been put forward to invert the sound speed profile from acoustic ***,the time cost of matched-field inversion may be very high in replica field *** studied the feasibility and robustness of an acoustic tomography scheme with matched-field processing in shallow water,and described the sound speed profile by empirical orthogonal *** analyzed the acoustic signals from a vertical line array in ASIAEX2001 in the East China Sea to invert sound speed profiles with estimated empirical orthogonal functions and a parallel genetic algorithm to speed up the *** results show that the inverted sound speed profiles are in good agreement with conductivity-temperature-depth ***,a posteriori probability analysis is carried out to verify the inversion results.

关键词： matched-field processing sound speed profile parallel genetic algorithm

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Optimal allocation of enterprise marketing resources based on hybrid parallel genetic algorithm and simulated annealing algorithm

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 2266-2278页

作者： Li, Min Zhengzhou Profess Tech Inst Elect & Informat Zhengzhou 451450 Henan Peoples R China

Aiming at minimizing the use of marketing resources, this article establishes the mathematical model of marketing resources allocation, designs the algorithm of marketing resources allocation, and compares the examples. An improved heuristic algorithm considering tilt angle matching is proposed and used as a local search algorithm for enterprise marketing resources. We design an innovative optimization strategy that incorporates the concept of tilt angle matching to enhance the local search efficiency of enterprise marketing resource allocation. In addition, we have introduced a novel parallel grouping genetic algorithm (PGGA), which utilizes grouping coding and exon crossover to further enhance the search and optimization efficiency of the solution. PGGA is improved by using adaptive parameters to form IPGGA, which improves the efficiency and convergence speed of enterprise marketing resource allocation. The annealing function of the simulated annealing algorithm is improved, and a model is constructed to solve the problem of enterprise marketing resource allocation. Simulated annealing algorithm is introduced to solve the problem of marketing resource allocation, and the framework of simulated annealing algorithm is analyzed. To solve the problem of fast decay rate of simulated annealing algorithm, Doppler effect function is used to optimize the algorithm. This article mainly uses qualitative and quantitative analysis methods to conduct in-depth research on enterprise marketing resource allocation. It focuses on the planning and allocation of enterprise marketing resources. Through IPGGA-ISAA research and analysis of all kinds of data of enterprise marketing resources, the present situation, efficiency, and main problems of enterprise marketing resources allocation are discussed and analyzed more deeply. Compared with other algorithms, IPGGA-ISAA can better analyze the causes of problems and provide better marketing resource allocation schemes for enterprises.

关键词： enterprise marketing resources optimize the configuration parallel genetic algorithm simulated annealing algorithm

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Selecting informative rules with parallel genetic algorithm in classification problem

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APPLIED MATHEMATICS AND COMPUTATION 2011年第7期218卷 3247-3264页

作者： Sarkar, Bikash Kanti Sana, Shib Sankar Chaudhuri, Kripasindhu BIT Dept Informat Technol Ranchi 835215 Jharkhand India Bhangar Mahavidyalaya CU Dept Math Bhangar 743502 WB India Jadavpur Univ Dept Math Kolkata 32 India

The classification system is very important for making decision and it has been attracted much attention of many researchers. Usually, the traditional classifiers are either domain specific or produce unsatisfactory results over classification problems with larger size and imbalanced data. Hence, genetic algorithms (GA) are recently being combined with traditional classifiers to find useful knowledge for making decision. Although, the main concerns of such GA-based system are the coverage of less search space and increase of computational cost with the growth of population. In this paper, a rule-based knowledge discovery model, combining C4.5 (a Decision Tree based rule inductive algorithm) and a new parallel genetic algorithm based on the idea of massive parallelism, is introduced. The prime goal of the model is to produce a compact set of informative rules from any kind of classification problem. More specifically, the proposed model receives a base method C4.5 to generate rules which are then refined by our proposed parallel GA. The strength of the developed system has been compared with pure C4.5 as well as the hybrid system (C4.5 + sequential genetic algorithm) on six real world benchmark data sets collected from UCI (University of California at Irvine) machine learning repository. Experiments on data sets validate the effectiveness of the new model. The presented results especially indicate that the model is powerful for volumetric data set. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Classification Accuracy C4.5 parallel genetic algorithm

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A parallel and non-parallel genetic algorithm for deconvolution of NMR spectra peaks

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2013年 125卷 147-152页

作者： Karakaplan, Mustafa Avcu, Fatih Mehmet Inonu Univ Art & Sci Fac Dept Chem Malatya Turkey Inonu Univ Inst Sci Dept Chem Malatya Turkey

Interpreting high resolution nuclear magnetic resonance (NMR) spectra of complex samples is investigated by the use of real valued parallel and nonparallel genetic algorithm based on stochastic search procedure. The population-centric crossover operators were used in real coded genetic algorithm (RCGA) based on some probability distributions. This paper also presents parallel genetic algorithms computations with different genetic immigration operators. Different results were found with respect to the problems, even at the different stages of the genetic process in the same problem. It is observed that the grid and centralized type genetic immigration (island models) were effective on the global optimization. The parallel and non-parallel algorithms were also applied for solving some multi-modal test problems. It is found that the island models achieve superior performance on multi-modal test problems and on deconvolution of complex NMR spectra. (C) 2013 Elsevier B.V. All rights reserved.

关键词： parallel genetic algorithm Peak deconvolution NMR Experimental chemistry data processing

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Estimation of frequency domain soil parameters of horizontally multilayered earth by using Cole-Cole model based on the parallel genetic algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第9期13卷 1746-1754页

作者： Li, Zhong-Xin Rao, Shao-Wei China Elect Power Res Inst Dept High Voltage Beijing 100192 Peoples R China

A method to estimate frequency domain soil parameters of horizontally multilayered earth is developed. Cole-Cole model of complex conductivity form is adopted to describe the frequency dependence of soil parameters. The estimation of frequency domain soil parameters includes two stages. In the first stage, soil-layered structure is determined under the DC field by the interpretation of resistivity sounding data. The obtained model parameters in this stage include the DC soil conductivity and thickness of soil layers. In the second stage, Cole-Cole model parameters are estimated based on the soil-layered model obtained in the first stage. The theoretical formula of complex apparent resistivity is derived from Green's function with considering dynamic-state field theory. Since dynamic-state field is considered in the inversion algorithm, this method can be extended into high-frequency domain. Discrete complex image method is used to calculate Sommerfeld integral quickly. In order to remove electromagnetic (EM) coupling between cables at high frequencies, a new electrode configuration method is proposed. genetic algorithm is improved by combining with parallel computing. The parallel genetic algorithm is applied to the optimisation of model parameters.

关键词： Green's function methods geophysical techniques soil electromagnetic wave propagation terrestrial electricity genetic algorithms electromagnetic field theory frequency domain soil parameters parallel genetic algorithm soil-layered structure soil-layered model high-frequency domain Cole-Cole model parameters horizontally multilayered Earth

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