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An adaptive genetic algorithm-based background elimination model for English text

SOFT COMPUTING 2022年第16期26卷 8133-8143页

作者： Tang, Xiaohui Shandong Univ Polit Sci & Law Jinan 250014 Shandong Peoples R China

In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. To enhance identification accuracy, a genetic algorithm is used to iterate the feature sets of angles and line segments, and the optimum features are ultimately generated via cross evolution of generations. The character library is then utilized as an input item for average grouping for trials, and the resulting feature sets are placed in a position matrix and compared one by one to the samples in the database. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, an

关键词： adaptive genetic algorithm English text Background elimination model

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Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

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Journal of Central South University 2010年第1期17卷 123-128页

作者：喻寿益邝溯琼 School of Information Science and Engineering Central South University

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.

关键词： adaptive genetic algorithm fuzzy rules auto-regulating crossover probability adjustment

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A Design of adaptive genetic algorithm-Based Optimized Power Amplifier for 5G Applications

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2024年第1期43卷 2-21页

作者： Delwar, Tahesin Samira Siddique, Abrar Aras, Unal Ryu, Jee Youl Pukyong Natl Univ Dept Intelligence Robot Engn Busan South Korea

In this paper, a novel adaptive genetic algorithm (AGA) is presented for the optimization goals. The presented AGA is discussed and implemented on a power amplifier (PA), which is designed for 24 GHz and 5G applications and fabricated in a 65-nm CMOS process. The PA is optimized by the AGA for the high power-added efficiency (PAE), i.e., optimum. In the AGA, we aimed to avoid the local optima and slow convergence rate that exist in the conventional genetic algorithm (CGA). In the AGA, we proposed a parameter tuning method to fine-tune the set of PA circuit component values for faster optimization of the PA to have a high PAE. The proposed AGA provides a significant speed in optimization process as compared to the CGA, and the execution time of the AGA is faster than that of the CGA. The proposed AGA is also verified, and its performance is compared with that of the CGA through multiple multidimensional mathematical benchmark functions. The PA performance parameters are measured, and the results showed that the optimized PA achieves a high gain of 29.9 dB. The P-sat of PA is measured as 14.21 dBm, and IIP3 is 13.8 dBm. The simulated result shows the optimized PA with the AGA has PAE of 49.7%, while that with the CGA has PAE of 47.5% at 24 GHz. The final measured PAE of AGA's optimized PA is 47.1%. The chip area of PA is 0.29 mm(2).

关键词： adaptive genetic algorithm Conventional genetic algorithm Power amplifier Circuit optimization 5G

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Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第14期80卷 21165-21202页

作者： Denis, R. Madhubala, P. Periyar Univ Dept Comp Sci Salem TN India Don Bosco Coll Dept Comp Sci Dharmapuri TN India

The exponential rise in the development of cloud computing environments in the healthcare field, the protection and confidentiality of the medical records become a primary concern for healthcare services applications. Today, health data stored in the cloud is highly confidential information concealed to avoid unauthorized access to protect the patient's information. As cloud-based medical data transmission becomes more common, it receives growing attention from researchers and academics. Despite the potential for misuse, medical data transmitted through unreliable networks can be manipulated or compromised. The current cryptosystems alone are not sufficient to deal with these issues, and hence this paper introduces a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is developed by combining either 2D Discrete Wavelet Transform 1 Level (2D-DWT-1 L) or 2D Discrete Wavelet Transform 2 Level (2D-DWT-2 L) steganography with the proposed hybrid encryption scheme. The hybrid encryption scheme is built by strategically applying Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms to secure diagnosis data to be embedded with the RGB channels of medical cover image. One of the key novelties is the use of an adaptive genetic algorithm for Optimal Pixel Adjustment Process (AGA-OPAP) that enriches data hiding ability as well as imperceptibility features. To evaluate the efficiency of the proposed model, numerical tests are performed. The results show that the proposed algorithm is capable of safely transmitting medical data. Comparison of results is carried out concerning the datasets with the state-of-the-art algorithm. In terms of various statistical measures, the results showed the superiority of the proposed algorithm, such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM), entropy, histogram, NPCR, UACI and embedding capacity. The prop

关键词： Encryption Decryption Data hiding Cloud environment Health information exchange adaptive genetic algorithm Steganalysis

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Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework

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IET GENERATION TRANSMISSION & DISTRIBUTION 2010年第12期4卷 1288-1298页

作者： Gupta, N. Swarnkar, A. Niazi, K. R. Bansal, R. C. Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

This study presents an efficient method for the multi-objective reconfiguration of radial distribution systems in fuzzy framework using adaptive genetic algorithm. The initial population for genetic algorithm is created using a heuristic approach and the genetic operators are adapted with the help of graph theory to generate feasible individuals. This avoids tedious mesh check and hence reduces the computational burden. The effectiveness of the proposed method is demonstrated on 70-bus test system and 136-bus real distribution system. The simulation results show that the proposed method is efficient and promising for multi-objective reconfiguration of radial distribution systems.

关键词： fuzzy framework adaptive genetic algorithm Distribution networks graph theory 70-bus test system genetic algorithms radial distribution systems 136-bus real distribution system Combinatorial mathematics Optimisation techniques power distribution multiobjective reconfiguration

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The solution of traffic flow organisation optimisation model based on adaptive genetic algorithm

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INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS 2022年第5期29卷 480-502页

作者： Zhang, Ji Lv, Hongxia Deng, Boer Wang, Wenxian Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Wuyi Univ Sch Railway Tracks & Transportat Jiangmen 529020 Peoples R China

In order to reduce the workload of the stations along the way, the train flow in the loading area is rationally organised. By designing a vehicle flow organisation method that increases the turnover speed of trucks, it helps to better coordinate the flow of goods and trains. The organisation of the three trucks in the loading area and the time consumption index are analysed, and the optimisation model of the train route adjustment is constructed based on the results to reduce the time consumption of train transportation. In order to ensure the spatial feasibility of the designed model in the application process, based on the improved genetic algorithm, the complexity of the built model and related constraints are considered, and an algorithm that restricts the space search is established. This algorithm can realise adaptive adjustment. The case analysis of Huangdao loading area proves the effect of this model.

关键词： car flow organisation in loading area vehicle hour consumption optimisation model spatial search strategy adaptive genetic algorithm

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An accurate flexible process planning using an adaptive genetic algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6435-6456页

作者： Haro, Eduardo H. Avalos, Omar Camarena, Octavio Cuevas, Erik Univ Guadalajara Dept Elect CUCEI Av Revoluc 1500 Guadalajara Jalisco Mexico

The increasing demand for products and services due to globalization has quickly increased through the years. Under such circumstances, the improvement in manufacturing processes has taken the attention of several areas of engineering. Different schemas have been introduced in the context of distributed manufacturing, such as the flexible use of tools, machines, and tool access directions which become a complex task considering the difficult combinatory process and rigorous restrictions. To overcome such complications, flexible process planning (FPP) has been treated as an optimization problem. Moreover, the problem difficulty compromises the proper balance between performance and computational cost which generates the proposal of different optimization techniques, statistical criteria, and hybridizations. Despite the good results of different methods, there are still several possibilities for improvement. In this work, a genetic algorithm (GA) is employed for an accurate FPP process where the GA operators are adapted in order to join up the combinatory optimization process of FPP with the main structure of GA (aGA). To carry out the experimentation, different scenarios of FPP problems using AND/OR networks, production time, and production cost are considered. The adapted genetic algorithm for flexible process planning (aGA-FPP) problems has shown competitive results regarding similar approaches and hybridizations reported in the literature.

关键词： adaptive genetic algorithm Flexible process planning Combinatory process Optimization process

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Multiple GEO satellites on-orbit repairing mission planning using large neighborhood search-adaptive genetic algorithm

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ADVANCES IN SPACE RESEARCH 2022年第2期70卷 286-302页

作者： Han, Peng Guo, Yanning Li, Chuanjiang Zhi, Hui Lv, Yueyong Harbin Inst Technol Harbin 150001 Peoples R China

This paper proposes a novel large neighborhood search-adaptive genetic algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning of geosynchronous orbit (GEO) satellites with mission deadline constraint. In the many-to-many on-orbit repairing scenario, several servicing spacecrafts and target satellites are located in GEO orbits which have different inclination, right ascension of ascending node (RAAN) and true anomaly. Each servicing spacecraft need to rendezvous with target satellites to perform repairing missions under limited fuel. The mission objective is to find the optimal servicing sequence and orbit rendezvous time of each servicing spacecraft to minimize total Delta eta cost of all servicing spacecrafts with all target satellites repaired. Firstly, a time-dependent orbital rendezvous strategy is proposed, which can handle the mission deadline constraint. Besides, it is also cost-effective compared with the existing strategy. Based on this strategy, the many-to-many on-orbit repairing mission planning model can be simplified to an integer programming problem, which is established based on the vehicle routing problem with time windows (VRPTW) model. In order to efficiently find a feasible optimal solution under complicated constraints, a hybrid adaptive genetic algorithm combining the large neighborhood search procedure is designed. The operations of "destroy" and "repair" are used on the elite individuals in each generation of the genetic algorithm to enhance local search capabilities. Finally, simulation results under several different scenarios show the effectiveness and superiority of the presented algorithm and orbital rendezvous strategy. (C) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Geosynchronous satellite Mission planning On-orbit repairing adaptive genetic algorithm Large neighborhood search

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Optimization design of irregular grooved texture on the surface of sliding pair based on adaptive genetic algorithm

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INDUSTRIAL LUBRICATION AND TRIBOLOGY 2023年第10期75卷 1208-1218页

作者： Shen, Zhongkai Li, Shaojun Wu, Zhenpeng Dong, Bowen Luo, Wenyan Zeng, Liangcai Hubei Polytech Univ Sch Mech & Elect Engn Huangshi Peoples R China Hubei Polytech Univ Hubei Key Lab Intelligent Conveying Technol & Devi Huangshi Peoples R China Wuhan Univ Sci & Technol Wuhan Peoples R China

PurposeThis study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization ***/methodology/approachUsing adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. genetic iterative methods were used to calculate the optimal texture structure. genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex *** about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component ***/valueSurface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often req

关键词： Irregular grooves Surface texture adaptive genetic algorithm Friction characteristics Optimization

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An adaptive genetic algorithm with dominated genes for distributed scheduling problems

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EXPERT SYSTEMS WITH APPLICATIONS 2005年第2期29卷 364-371页

作者： Chan, FTS Chung, SH Chan, PLY Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China

This paper proposes an adaptive genetic algorithm for distributed scheduling problems in multi-factory and multi-product environment. Distributed production strategy enables factories to be more focused on their core product types, to achieve better quality, to reduce production cost, and to reduce management risk. However, when comparing with single-factory production, scheduling problems involved in multi-factory one are more complicated, since different jobs distributed to different factories will have different production scheduling, consequently affect the performance of the supply chain. Distributed scheduling problems deal with the assignment of jobs to suitable factories and determine their production scheduling accordingly. In this paper, a new crossover mechanism named dominated gene crossover will be introduced to enhance the performance of genetic search, and eliminate the problem of determining optimal crossover rate. A number of experiments have been carried out. For the comparison purpose, five multi-factory models have been solved by different well known optimization approaches. The results indicate that significant improvement could be obtained by the proposed algorithm. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： adaptive genetic algorithm dominated genes scheduling problems

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