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Nested Genetic Algorithm for Resolving Overlapped Spectral Bands 认领 引用
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作者 Xiu Qi ZHANG Yun Hui ZENG +1 位作者 Jian Bin ZHENG Hong GAO 《Chinese Chemical Letters》 SCIE CAS 2000年第7期603-604,共2页
A nested genetic algorithm, including genetic parameter level and genetic implemented level for peak parameters, was proposed and applied for resolving overlapped spectral bands. By the genetic parameter level, parame... A nested genetic algorithm, including genetic parameter level and genetic implemented level for peak parameters, was proposed and applied for resolving overlapped spectral bands. By the genetic parameter level, parameters of generic algorithm were optimized; moreover, the number of overlapped peaks was determined simultaneously Then parameters of individual peaks were computed with the genetic implemented level. 展开更多
关键词 nested genetic algorithm resolving overlapped bands spectra
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Distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm 认领 引用 被引量：4
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作者 Yaozhong Zhang Lei Zhang Zhiqiang Du 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2015年第6期1236-1243,共8页
A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple... A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload. 展开更多
关键词 distributed collaborative planning blackboard decision maker （DM） nested genetic algorithm （NGA）.
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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm 认领 引用 被引量：16
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作者 毛勇 周晓波 +2 位作者 皮道映 孙优贤 WONG Stephen T.C. 《Journal of Zhejiang University-SCIENCE B》 EI CAS 2005年第10期961-973,共13页
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result... In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes. 展开更多
关键词 Gene selection, Support vector machine (SVM), Recursive feature elimination (RFE), Genetic algorithm (GA),Parameter selection
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Optimization Configuration Method for Grid-Side Grid-Forming Energy Storage System Based on Genetic Algorithm 认领 引用 被引量：1
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作者 Yuqian Qi Yanbo Che +2 位作者 Liangliang Liu Jiayu Ni Shangyuan Zhang 《Energy Engineering》 EI 2025年第10期3999-4017,共19页
The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-... The process of including renewable energy sources in power networks is moving quickly,so the need for innovative configuration solutions for grid-side ESS has grown.Among the new methods presented in this paper is GA-OCESE,which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks.This is one of the methods suggested in this study,which aims to enhance the sizing,positioning,and operational characteristics of structured ESS under dynamic grid conditions.Particularly,the aim is to maximize efficiency.A multiobjective genetic algorithm,the GA-OCESE framework,considers all these factors simultaneously.Besides considering cost-efficiency,response time,and energy use,the system also considers all these elements simultaneously.This enables it to effectively react to load uncertainty and variations in inputs connected to renewable sources.Results of an experimental assessment conducted on a standardized grid simulation platform indicate that by increasing energy use efficiency by 17.6%and reducing peak-load effects by 22.3%,GA-OCESE outperforms previous heuristic-based methods.This was found by contrasting the outcomes of the assessment with those of the evaluation.The results of the assessment helped to reveal this.The proposed approach will provide utility operators and energy planners with a decision-making tool that is both scalable and adaptable.This technology is particularly well-suited for smart grids,microgrid systems,and power infrastructures that heavily rely on renewable energy.Every technical component has been carefully recorded to ensure accuracy,reproducibility,and relevance across all power systems engineering software uses.This was done to ensure the program’s relevance. 展开更多
关键词 Energy storage system(ESS) genetic algorithm(GA) grid optimization smart grid renewable energy integration multi-objective optimization
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Use the Power of a Genetic Algorithm to Maximize and Minimize Cases to Solve Capacity Supplying Optimization and Travelling Salesman in Nested Problems 认领 引用
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作者 Ali Abdulhafidh Ibrahim Hajar Araz Qader Nour Ai-Huda Akram Latif 《Journal of Computer and Communications》 2023年第3期24-31,共8页
Using Genetic Algorithms (GAs) is a powerful tool to get solution to large scale design optimization problems. This paper used GA to solve complicated design optimization problems in two different applications. The ai... Using Genetic Algorithms (GAs) is a powerful tool to get solution to large scale design optimization problems. This paper used GA to solve complicated design optimization problems in two different applications. The aims are to implement the genetic algorithm to solve these two different (nested) problems, and to get the best or optimization solutions. 展开更多
关键词 Genetic Algorithm Capacity Supplying Optimization Traveling Salesman Problem Nested Problems
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Genetic Algorithms to the Nesting Problem in the Leather Manufacturing Industry 认领 引用
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作者 张玉萍 蒋寿伟 尹忠慰 《Journal of Donghua University(English Edition)》 EI CAS 2005年第1期90-96,共7页
The nesting problem in the leather manufacturing is the problem of placing a set of irregularly shaped pieces (called stencils) on a set of irregularly shaped surfaces (called leathers sheets). This paper presents a n... The nesting problem in the leather manufacturing is the problem of placing a set of irregularly shaped pieces (called stencils) on a set of irregularly shaped surfaces (called leathers sheets). This paper presents a novel and promising processing approach. After the profile of leather sheets and stencils is obtained with digitizer, the discretization makes the processing independent of the specific geometrical information. The constraints of profile are regarded thoroughly. A heuristic bottom-left placement strategy is employed to sequentially locate stencils on sheets. The optimal placement sequence and rotation are deterimined by genetic algorithms (GA). A natural concise encoding method is developed to satisfy all the possible requirements of the leather nesting problem. The experimental results show that the proposed algorithm can not only be applied to the normal two-dimensional nesting problem, but also especially suitable for the placement of multiple two-dimensional irregular stencils on multiple two-dimensional irregular sheets. 展开更多
关键词 leather nesting genetic algorithms two-dimensional geometry irregular discretization.
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Two-Dimensional Nesting System Based on Hybrid Genetic Algorithm 认领 引用
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作者 WU Qingming YANG Wei ZHANG Qiang ZHOU Junjie 《Wuhan University Journal of Natural Sciences》 CAS 2009年第1期60-64,共5页
According to the cutting stock problem of 2-dimensional shapes, a nesting system （NS） based on hybrid genetic algorithm （HGA） is established. The system optimizes the sequence and angles of polygons with hybrid Ge... According to the cutting stock problem of 2-dimensional shapes, a nesting system （NS） based on hybrid genetic algorithm （HGA） is established. The system optimizes the sequence and angles of polygons with hybrid Genetic Algorithm to accomplish the superior solution. It nests the irregular shape directly without covering irregular shapes with a rectangle. It also improves the decoding strategy of 2-dimensional shapes nesting based on the classical bottom-left strategy, makes the new strategy be universal to convex polygons, concave polygons and line-circular composted polygons. 展开更多
关键词 nesting system hybrid genetic algorithm （HGA） regular and circular polygon bottom-left strategy
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An improved genetic algorithm for causal discovery 认领 引用
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作者 MAO Tengjiao BU Xianjin +2 位作者 CAI Chunxiao LU Yue DU Jing 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第3期768-777,共10页
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to... The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm. 展开更多
关键词 genetic algorithm(GA) causal discovery convergence rate fitness function mutation operator
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FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM 认领 引用 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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Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA) 认领 引用 被引量：1
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作者 Mohammad Najjarpour Hossein Jalalifar 《Journal of Applied Mathematics and Physics》 2018年第8期1581-1595,共15页
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid... The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack. 展开更多
关键词 Wing Crack Fairhorst-Cook Model Sensitivity Analysis Optimization Particle Swarm Intelligence (PSO) Ant Colony Optimization (ACO) Genetic Algorithm (GA)
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Improved genetic algorithm for nonlinear programming problems 认领 引用 被引量：10
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作者 Kezong Tang Jingyu Yang +1 位作者 Haiyan Chen Shang Gao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第3期540-546,共7页
An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector w... An improved genetic algorithm（IGA） based on a novel selection strategy to handle nonlinear programming problems is proposed.Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value,the degree of constraints violations and the number of constraints violations.It is easy to distinguish excellent individuals from general individuals by using an individuals＇ feature vector.Additionally,a local search（LS） process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions.The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions.Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms. 展开更多
关键词 genetic algorithm（GA） nonlinear programming problem constraint handling non-dominated solution optimization problem.
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Memristive network-based genetic algorithm and its application to image edge detection 认领 引用 被引量：9
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作者 YU Yongbin YANG Chenyu +3 位作者 DENG Quanxin NYIMA Tashi LIANG Shouyi ZHOU Chen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第5期1062-1070,共9页
This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection alg... This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection algorithm where image pixels are defined as individuals of population.First,the computing model of MNGA is designed to perform mem-computing,which brings new possibility of the hardware implementation of GA.Secondly,MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed.Finally,simulation results demonstrate that the figure of merit(FoM)of our model is better than the latest memristor-based swarm intelligence.In summary,a new way is found to build proper matching of memristor to GA and aid image edge detection. 展开更多
关键词 memristive network(MN) genetic algorithm(GA) edge detection mem-computing
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Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm 认领 引用 被引量：18
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作者 Hong-li QI Hui ZHAO +1 位作者 Wei-wen LIU Hai-bo ZHANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第8期1205-1212,共8页
A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The pa... A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS. 展开更多
关键词 Grating eddy current displacement sensor （GECDS） Artificial neural network （ANN） Genetic algorithm （GA） Parameters optimization Nonlinearity error
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Optimal design of pressure vessel using an improved genetic algorithm 认领 引用 被引量：6
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作者 Peng-fei LIU Ping XU +1 位作者 Shu-xin HAN Jin-yang ZHENG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第9期1264-1269,共6页
As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weigh... As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method. 展开更多
关键词 Pressure vessel Optimal design Genetic algorithm （GA） Simulated annealing （SA） Finite element analysis （FEA）
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NOVEL APPROACH TO LOCATOR LAYOUT OPTIMIZATION BASED ON GENETIC ALGORITHM 认领 引用 被引量：7
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作者 吴铁军 楼佩煌 秦国华 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2011年第2期176-182,共7页
Proper fixture design is crucial to obtain the better product quality according to the design specification during the workpiece fabrication. Locator layout planning is one of the most important tasks in the fixture d... Proper fixture design is crucial to obtain the better product quality according to the design specification during the workpiece fabrication. Locator layout planning is one of the most important tasks in the fixture design process. However, the design of a fixture relies heavily on the designerts expertise and experience up to now. Therefore, a new approach to loeator layout determination for workpieces with arbitrary complex surfaces is pro- posed for the first time. Firstly, based on the fuzzy judgment method, the proper locating reference and locator - numbers are determined with consideration of surface type, surface area and position tolerance. Secondly, the lo- cator positions are optimized by genetic algorithm（GA）. Finally, a typical example shows that the approach is su- perior to the experiential method and can improve positioning accuracy effectively. 展开更多
关键词 locator layout locating error fuzzy judgment genetic algorithm（GA）
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Frequency selective surface structure optimized by genetic algorithm 认领 引用 被引量：5
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作者 卢俊 汪剑波 孙贯成 《Chinese Physics B》 SCIE EI CAS 2009年第4期1598-1600,共3页
Frequency selective surface （FSS） is a two-dimensional periodic structure which has promiaent characteristics of bandpass or bandbloek when interacting with electromagnetic waves. In this paper, the thickness, the d... Frequency selective surface （FSS） is a two-dimensional periodic structure which has promiaent characteristics of bandpass or bandbloek when interacting with electromagnetic waves. In this paper, the thickness, the dielectric constant, the element graph and the arrangement periodicity of an FSS medium are investigated by Genetic Algorithm （GA） when an electromagnetic wave is incident on the FSS at a wide angle, and an optimized FSS structure and transmission characteristics are obtained. The results show that the optimized structure has better stability in relation to incident angle of electromagnetic wave and preserves the stability of centre frequency even at an incident angle as large as 80°, thereby laying the foundation for the application of FSS to curved surfaces at wide angles. 展开更多
关键词 frequency selective surface （FSS） genetic algorithm （GA） optimization
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A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Pickup and Delivery 认领 引用 被引量：10
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作者 Fang-Geng Zhao Jiang-Sheng Sun +1 位作者 Su-Jian Li Wei-Min Liu 《International Journal of Automation and computing》 2009年第1期97-102,共6页
In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that... In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics. 展开更多
关键词 Genetic algorithm （GA） pheromone-based crossover local search pickup and delivery traveling salesman problem（TSP）.
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Robot stereo vision calibration method with genetic algorithm and particle swarm optimization 认领 引用 被引量：2
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作者 汪首坤 李德龙 +1 位作者 郭俊杰 王军政 《Journal of Beijing Institute of Technology》 EI CAS 2013年第2期213-221,共9页
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ... Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation. 展开更多
关键词 robot stereo vision camera calibration genetic algorithm （GA） particle swarm opti-mization （PSO） hybrid intelligent optimization
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A hybrid genetic-simulated annealing algorithm for optimization of hydraulic manifold blocks 认领 引用 被引量：9
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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)》 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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Worst-case tolerance analysis on array antenna based on chaos-genetic algorithm 认领 引用 被引量：2
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作者 Hao Yuan Dan Songt +1 位作者 Qiangfeng Zhou Huaping Xu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第6期824-830,共7页
This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic ... This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic algorithm （CGA） is proposed. The proposed method utilizes chaos to optimize initial population for the genetic algorithm （GA） and introduces chaotic disturbance into the genetic mutation, thereby improving the ability of the GA to search for the global optimum. Numerical simulations demonstrate that the accuracy and stability of the worst-case analysis of the proposed approach are superior to the GA. And the proposed algorithm can be used easily for the error tolerant design of antenna arrays. 展开更多
关键词 genetic algorithm （GA） array antenna tolerance anal-ysis chaos disturbance logistic map
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