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Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means
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作者 Bo-Han Wu Ran-Hong Xie +3 位作者 Li-Zhi Xiao Jiang-Feng Guo Guo-Wen Jin Jian-Wei Fu 《Petroleum Science》 SCIE EI CSCD 2023年第5期2747-2758,共12页
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig... In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method. 展开更多
关键词 Tight sandstone Integrated reservoir classification Principal component analysis simulated annealing genetic algorithm Fuzzy cluster means
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An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode
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作者 Jiamin Xiang Ying Zhang +1 位作者 Xiaohua Cao Zhigang Zhou 《Computers, Materials & Continua》 SCIE EI 2023年第12期3443-3466,共24页
This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aim... This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time. 展开更多
关键词 AGV scheduling composite operation mode genetic algorithm simulated annealing algorithm task advance evaluation strategy
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A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling 被引量:12
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作者 SHU Wanneng ZHENG Shijue 《Wuhan University Journal of Natural Sciences》 CAS 2006年第5期1378-1382,共5页
In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem i... In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing. 展开更多
关键词 grid computing task scheduling genetic algorithm simulated annealing PGSAHA algorithm
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Study on Multi-stream Heat Exchanger Network Synthesis with Parallel Genetic/Simulated Annealing Algorithm 被引量:13
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作者 魏关锋 姚平经 +1 位作者 LUOXing ROETZELWilfried 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2004年第1期66-77,共12页
The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one opt... The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS. 展开更多
关键词 multi-stream heat exchanger network synthesis non-isothermal mixing mixed integer nonlinear programming model genetic algorithm simulated annealing algorithm hybrid algorithm
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Using genetic/simulated annealing algorithm to solve disassembly sequence planning 被引量:5
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作者 Wu Hao Zuo Hongfu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第4期906-912,共7页
Disassembly sequence planning (DSP) plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassem... Disassembly sequence planning (DSP) plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient. 展开更多
关键词 disassembly sequence planning disassembly hybrid graph connection matrix precedence matrix binary-tree algorithms simulated annealing algorithm genetic algorithm.
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A hybrid genetic-simulated annealing algorithm for optimization of hydraulic manifold blocks 被引量:7
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作者 刘万辉 田树军 +1 位作者 贾春强 曹宇宁 《Journal of Shanghai University(English Edition)》 CAS 2008年第3期261-267,共7页
This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks (HMB). Due to the limitation o... This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks (HMB). Due to the limitation of its local search ability of genetic algorithm (GA) in solving a massive combinatorial optimization problem, simulated annealing (SA) is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality. 展开更多
关键词 hydraulic manifold blocks (HMB) genetic algorithm (GA) simulated annealing (SA) optimal design
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FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM 被引量:6
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作者 Yang Meng A.E.A. Almaini Wang Pengjun 《Journal of Electronics(China)》 2006年第4期632-636,共5页
Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it... Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool. 展开更多
关键词 genetic algorithm (GA) simulated annealing (SA) PLACEMENT FPGA EDA
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Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning 被引量:3
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作者 高红民 周惠 +1 位作者 徐立中 石爱业 《Journal of Central South University》 SCIE EI CAS 2014年第1期262-271,共10页
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning (MIL). The band selection method was proposed from subspace decom... A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning (MIL). The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome. 展开更多
关键词 hyperspectral remote sensing images simulated annealing genetic algorithm support vector machine band selection multiple instance learning
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Optimal control of cobalt crust seabedmining parameters based on simulated annealing genetic algorithm 被引量:2
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作者 夏毅敏 张刚强 +2 位作者 聂四军 卜英勇 张振华 《Journal of Central South University》 SCIE EI CAS 2011年第3期650-657,共8页
Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head (the feed speed, the axes height of collecting hea... Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head (the feed speed, the axes height of collecting head, and the rotate speed) are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm (SAGA), the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn. 展开更多
关键词 cobalt crust mining parameter specific energy consumption simulated annealing genetic algorithm
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Resolution of Resource Contentions in the CCPM-MPL Using Simulated Annealing and Genetic Algorithm 被引量:1
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作者 Hajime Yokoyama Hiroyuki Goto 《American Journal of Operations Research》 2016年第6期480-488,共9页
This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known ... This research aims to plan a “good-enough” schedule with leveling of resource contentions. We use the existing critical chain project management-max-plus linear framework. Critical chain project management is known as a technique used to both shorten the makespan and observe the due date under limited resources;the max-plus linear representation is an approach for modeling discrete event systems as production systems and project scheduling. If a contention arises within a single resource, we must resolve it by appending precedence relations. Thus, the resolution framework is reduced to a combinatorial optimization. If we aim to obtain the exact optimal solution, the maximum computation time is longer than 10 hours for 20 jobs. We thus experiment with Simulated Annealing (SA) and Genetic Algorithm (GA) to obtain an approximate solution within a practical time. Comparing the two methods, the former was beneficial in computation time, whereas the latter was better in terms of the performance of the solution. If the number of tasks is 50, the solution using SA is better than that using GA. 展开更多
关键词 Critical Chain Project Management Max-Plus Algebra CCPM-MPL simulated annealing genetic algorithm
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Discrete channel modelling based on genetic algorithm and simulated annealing for training hidden Markov model
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作者 赵知劲 郑仕链 +1 位作者 徐春云 孔宪正 《Chinese Physics B》 SCIE EI CAS CSCD 2007年第6期1619-1623,共5页
Hidden Maxkov models (HMMs) have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm (GA) and simulated annealing (SA) to train HMM for dis... Hidden Maxkov models (HMMs) have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm (GA) and simulated annealing (SA) to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA's ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method. 展开更多
关键词 hidden Markov model discrete channel model genetic algorithm simulated annealing
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Revenue Optimization of Pipelines Construction and Operation Management Based on Quantum Genetic Algorithm and Simulated Annealing Algorithm
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作者 Kang Tan 《Journal of Applied Mathematics and Physics》 2018年第6期1215-1229,共15页
For the optimization of pipelines, most researchers are mainly concerned with designing the most reasonable section to meet the requirements of strength and stiffness, and at the same time reduce the cost as much as p... For the optimization of pipelines, most researchers are mainly concerned with designing the most reasonable section to meet the requirements of strength and stiffness, and at the same time reduce the cost as much as possible. It is undeniable that they do achieve this goal by using the lowest cost in design phase to achieve maximum benefits. However, for pipelines, the cost and incomes of operation management are far greater than those in design phase. Therefore, the novelty of this paper is to propose an optimization model that considers the costs and incomes of the construction and operation phases, and combines them into one model. By comparing three optimization algorithms (genetic algorithm, quantum genetic algorithm and simulated annealing algorithm), the same optimization problem is solved. Then the most suitable algorithm is selected and the optimal solution is obtained, which provides reference for construction and operation management during the whole life cycle of pipelines. 展开更多
关键词 QUANTUM genetic algorithm simulated annealing algorithm Pipelines CONSTRUCTION MANAGEMENT Operation OPTIMIZATION
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A Computational Comparison between Optimization Techniques for Wells Placement Problem: Mathematical Formulations, Genetic Algorithms and Very Fast Simulated Annealing
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作者 Ghazi D. AlQahtani Ahmed Alzahabi +1 位作者 Timothy Spinner Mohamed Y. Soliman 《Journal of Materials Science and Chemical Engineering》 2014年第10期59-73,共15页
This study considers several computational techniques for solving one formulation of the wells placement problem (WPP). Usually the wells placement problem is tackled through the combined efforts of many teams using c... This study considers several computational techniques for solving one formulation of the wells placement problem (WPP). Usually the wells placement problem is tackled through the combined efforts of many teams using conventional approaches, which include gathering seismic data, conducting real-time surveys, and performing production interpretations in order to define the sweet spots. This work considers one formulation of the wells placement problem in heterogeneous reservoirs with constraints on inter-well spacing. The performance of three different types of algorithms for optimizing the well placement problem is compared. These three techniques are: genetic algorithm, simulated annealing, and mixed integer programming (IP). Example case studies show that integer programming is the best approach in terms of reaching the global optimum. However, in many cases, the other approaches can often reach a close to optimal solution with much more computational efficiency. 展开更多
关键词 WELLS PLACEMENT Optimization INTEGER Programming simulated annealing genetic algorithm
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Investigation into the Computational Costs of Using Genetic Algorithm and Simulated Annealing for the Optimization of Explicit Friction Factor Models
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作者 Sunday Boladale Alabi Abasiyake Uku Ekpenyong 《Journal of Materials Science and Chemical Engineering》 CAS 2022年第12期1-9,共9页
Research reports show that the accuracies of many explicit friction factor models, having different levels of accuracies and complexities, have been improved using genetic algorithm (GA), a global optimization approac... Research reports show that the accuracies of many explicit friction factor models, having different levels of accuracies and complexities, have been improved using genetic algorithm (GA), a global optimization approach. However, the computational cost associated with the use of GA has yet to be discussed. In this study, the parameters of sixteen explicit models for the estimation of friction factor in the turbulent flow regime were optimized using two popular global search methods namely genetic algorithm (GA) and simulated annealing (SA). Based on 1000 interval values of Reynolds number (Re) in the range of and 100 interval values of relative roughness () in the range of , corresponding friction factor (f) data were obtained by solving Colebrook-White equation using Microsoft Excel spreadsheet. These data were then used to modify the parameters of the selected explicit models. Although both GA and SA led to either moderate or significant improvements in the accuracies of the existing friction factor models, SA outperforms the GA. Moreover, the SA requires far less computational time than the GA to complete the corresponding optimization process. It can therefore be concluded that SA is a better global optimizer than GA in the process of finding an improved explicit friction factor model as an alternative to the implicit Colebrook-White equation in the turbulent flow regime. 展开更多
关键词 genetic algorithm simulated annealing Global Optimization Explicit Friction Factor Computational Cost
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Surface/Surface Intersection Using Simulated Annealing Genetic Algorithm
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作者 唐敏 《High Technology Letters》 EI CAS 2000年第4期39-45,共7页
The genetic algorithm and marching method are integrated into a novel algorithm to solve the surface intersection problem. By combining genetic algorithm with local searching method the efficiency of evolution is grea... The genetic algorithm and marching method are integrated into a novel algorithm to solve the surface intersection problem. By combining genetic algorithm with local searching method the efficiency of evolution is greatly improved. By fully utilizing the global searching ability and instinct attribute for parallel computation of genetic algorithm and the local rapid convergency of marching method, the algorithm can compute the intersection robustly and generate correct topology of intersection curves. The details of the new algorithm are discussed here. 展开更多
关键词 Surface intersection Marching method simulated annealing genetic algorithm
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Coherent Doppler lidar wind retrieval for a typhoon based on the genetic simulated annealing algorithm
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作者 刘珍 原禄城 +6 位作者 汤杰 张云鹏 刘继桥 竹孝鹏 杨巨鑫 时伟 陈卫标 《Chinese Optics Letters》 SCIE EI CAS CSCD 2024年第4期1-7,共7页
A method of spectrum estimation based on the genetic simulated annealing(GSA)algorithm is proposed,which is applied to retrieve the three-dimensional wind field of typhoon Nangka observed by our research group.Compare... A method of spectrum estimation based on the genetic simulated annealing(GSA)algorithm is proposed,which is applied to retrieve the three-dimensional wind field of typhoon Nangka observed by our research group.Compared to the genetic algorithm(GA),the GSA algorithm not only extends the detection range and guarantees the accuracy of retrieval results but also demonstrates a faster retrieval speed.Experimental results indicate that both the GA and GSA algorithms can enhance the detection range by 35%more than the least squares method.However,the convergence speed of the GSA algorithm is 17 times faster than that of the GA,which is more beneficial for real-time data processing. 展开更多
关键词 coherent Doppler lidar three-dimensional wind field retrieval genetic simulated annealing algorithm spectrum estimation TYPHOON
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Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system 被引量:1
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作者 贺建军 喻寿益 钟掘 《Journal of Central South University of Technology》 2003年第4期359-363,共5页
A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that... A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters. 展开更多
关键词 genetic algorithm simulated annealing algorithm annealing-genetic algorithm complex electro-mechanical system PARAMETER tuning OPTIMAL control
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Ship Weather Routing Based on Hybrid Genetic Algorithm Under Complicated Sea Conditions
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作者 ZHOU Peng ZHOU Zheng +1 位作者 WANG Yan WANG Hongbo 《Journal of Ocean University of China》 SCIE CAS CSCD 2023年第1期28-42,共15页
Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key ro... Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies. 展开更多
关键词 genetic algorithm simulated annealing algorithm weather routing ship speed loss
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SOI MOSFET Model Parameter Extraction via a Compound Genetic Algorithm 被引量:2
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作者 李瑞贞 李多力 +2 位作者 杜寰 海潮和 韩郑生 《Journal of Semiconductors》 EI CAS CSCD 北大核心 2006年第5期796-803,共8页
We improve the genetic algorithm by combining it with a simulated annealing algorithm. The improved algorithm is used to extract model parameters of SOI MOSFETs, which are fabricated with standard 1.2μm CMOS/SOI tech... We improve the genetic algorithm by combining it with a simulated annealing algorithm. The improved algorithm is used to extract model parameters of SOI MOSFETs, which are fabricated with standard 1.2μm CMOS/SOI technology developed by the Institute of Microelectronics of the Chinese Academy of Sciences. The simulation results using this model are in excellent agreement with experimental results. The precision is improved noticeably compared to commercial software. This method requires neither a deeper understanding of SOl MOSFETs model nor more complex computations than conventional algorithms used by commercial software. Comprehensive verification shows that this model is applicable to a very large range of device sizes. 展开更多
关键词 SOI parameter extraction genetic algorithm simulated annealing algorithm
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基于GRA-GASA-SVM的煤层瓦斯含量预测方法研究 被引量:2
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作者 田水承 任治鹏 马磊 《煤炭技术》 CAS 2024年第1期114-118,共5页
为提升煤层瓦斯含量预测精度,提出一种采用遗传模拟退火算法混合优化支持向量机(SVM)参数的瓦斯含量预测模型(GRA-GASA-SVM模型)。该模型将GA和SA整合为遗传模拟退火算法协同优化SVM的参数,以解决传统网格寻优算法取值范围无法确定和单... 为提升煤层瓦斯含量预测精度,提出一种采用遗传模拟退火算法混合优化支持向量机(SVM)参数的瓦斯含量预测模型(GRA-GASA-SVM模型)。该模型将GA和SA整合为遗传模拟退火算法协同优化SVM的参数,以解决传统网格寻优算法取值范围无法确定和单一智能算法优化程度有限等问题。利用灰色关联分析(GRA)压缩数据集维度,建立瓦斯含量预测参数体系并作为GASA-SVM的输入数据集。结果表明:SVM模型、GA-SVM模型和GASA-SVM模型10折交叉验证瓦斯含量预测总平均相对误差分别为15.98%、13.55%和10.58%。相比SVM模型和GA-SVM模型,GASA-SVM模型预测稳定性更优、预测精准度更高且对新样本泛化能力更强。 展开更多
关键词 遗传算法(GA) 模拟退火算法(SA) 支持向量机(SVM) 煤层瓦斯含量 灰色关联分析(GRA)
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