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An accurate flexible process planning using an adaptive genetic algorithm

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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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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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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Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第5期29卷 584-589页

作者： Jerald, J. Asokan, P. Saravanan, R. Rani, A. Delphin Carolina Deemed Univ SASTRA Sch Mech Engn Thanjavur 613402 India Natl Inst Technol Dept Prod Engn Tiruchirappalli 625015 India JJ Coll Engn & Technol Dept Mech Engn Tiruchirappalli 625009 India PR Engn Coll Dept Comp Sci & Engn Thanjavur 613403 India

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized;as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm.

关键词： adaptive genetic algorithm automatic guided vehicles flexible manufacturing system genetic algorithm and scheduling

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Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization

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APPLIED SOFT COMPUTING 2008年第4期8卷 1607-1624页

作者： Balamurugan, R. Ramakrishnan, C. V. Singh, Nidur Indian Inst Technol Dept Appl Mech New Delhi India

genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated. (c) 2008 Published by Elsevier B.V.

关键词： topology optimization genetic algorithm adaptive genetic algorithm island genetic algorithm global optimal solution

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Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第6期29卷 1273-1285页

作者： Yan, Bo Yan, Chang Long, Feng Tan, Xing-Chao South China Univ Technol Sch Econ & Commerce Guangzhou 510006 Guangdong Peoples R China

Storage is an important part of commodity circulation. A certain amount of material must be stored to meet the needs of social production and consumption within a certain time to maintain the smooth process of social reproduction. This study focuses on warehousing optimization and goods location assignment when electronic products are stored in a stereoscopic storehouse. Moreover, this study is based on a theoretical study on genetic algorithm. On the basis of the background of the current warehouse management and cargo distribution of LCM module products warehouse belonging to W company, this study uses the dynamic goods location assignment strategy of stochastic inventory, and builds a multi-objective goods location assignment model of a stereoscopic warehouse. To simplify the calculations and improve the efficiency, we conduct a Matlab simulation on the basis of practical data by adopting a modified genetic operator and converting multi-objective optimization by the changing weight coefficient. The adaptive genetic algorithm can be used to make a multi-objective goods location assignment model that efficiently converges to the optimal solution.

关键词： Stereoscopic warehouse Warehousing goods location assignment Goods location optimization adaptive genetic algorithm

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Optimal Energy Management System for Grid-Tied Microgrid: An Improved adaptive genetic algorithm

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IEEE ACCESS 2023年 11卷 117351-117361页

作者： Majeed, Muhammad Asghar Phichisawat, Sotdhipong Asghar, Furqan Hussan, Umair Chulalongkorn Univ Fac Engn Dept Elect Engn Pathumwan 10330 Bangkok Thailand Univ Agr Faisalabad UAF Fac Agr Engn & Technol Dept Energy Syst Engn Faisalabad 38000 Pakistan Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518000 Peoples R China

Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.

关键词： Microgrids Costs Renewable energy sources Optimization genetic algorithms Mathematical models Batteries Peak to average power ratio adaptive genetic algorithm grid-tied microgrid bidirectional converter cost optimization renewable energy resources peak average ratio load scheduling resource allocation

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Focusing evaluation method based on wavelet transform and adaptive genetic algorithm

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OPTICAL ENGINEERING 2012年第2期51卷 023201-023201页

作者： Yin, Aijun Chen, Benqian Zhang, Yi Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Sch Mech Engn Chongqing 400044 Peoples R China

The focusing evaluation method based on wavelet transform and adaptive genetic algorithm (AGA) is developed in this paper. First, db7 wavelet transform is performed and the corresponding images are evaluated. Afterward, the optimal evaluation region is searched automatically in 2D wavelet coefficients by AGA. Finally, the criteria function based on the weighted M-WT(2) operator is used to evaluate the focusing condition. The wavelet base, order number of vanishing moments, and wavelet decomposition level are the key factors in constructing the operator. The parameters of new focus measure operator are analyzed and verified through experimental methods. The comparative experiments with other methods show that the AGA method not only could find the approximate optimal evaluation window quickly and accurately, but it has better generality as well. The weighted evaluation function of the M-WT(2) operator based on the db7 wavelet has higher sensitivity and better real-time performance. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/***.51.2.023201]

关键词： wavelet transform adaptive genetic algorithm focusing evaluation evaluation window

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Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

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SOFT COMPUTING 2023年第3期27卷 1599-1613页

作者： Gao, Yunguang Ma, Changlin Sheng, An Hunan Trinity Ind Vocat & Tech Coll Changsha Changsha County Peoples R China Xian Res Inst Hitech Xian Peoples R China

Developing fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This paper mainly studies and explores the compound fault diagnosis for the cooling dehumidifier. Firstly, the dehumidifier data acquisition system is built, which can be applied to the data acquisition, work status simulation and fault diagnosis. Secondly, a compound fault diagnosis model based on radial basis function neural network (RBFNN) improved by kernel principle component analysis (KPCA) and adaptive genetic algorithm (AGA) is proposed. Aiming at the problems that the selection of RBF width depends on expert experience and knowledge, the network structure scale is large or the training speed is slow in the conventional RBFNN models;on the one hand, AGA and K-means clustering algorithm are employed to automatically optimize the RBF width, the number of hidden layer neurons and the neuron centers, which guarantees the model has small structure and fast running speed on the premise of sufficient output precision, and the local optimization problem of K-means clustering algorithm is well suppressed by AGA group calculation;this also ensures the performance of RBFNN;on the other hand, KPCA is employed to reduce the dimension of the model input data, which not only effectively extract the nonlinear features, but also further simplify the network structure. Finally, the proposed method is validated and compared with the conventional models. The results show that this proposed model can not only be effectively applied to the dehumidifier compound fault diagnosis, but also has prominent application advantages.

关键词： Cooling dehumidifier Compound fault RBF neural network Kernel principle component analysis adaptive genetic algorithm

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Meander Line Antenna Design Using an adaptive genetic algorithm

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IEEE TRANSACTIONS ON MAGNETICS 2013年第5期49卷 1889-1892页

作者： Sato, Yuki Campelo, Felipe Igarashi, Hajime Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan Univ Fed Minas Gerais Dept Engn Eletr BR-31270901 Belo Horizonte MG Brazil

This paper presents the optimization of a meander line antenna finite-element model by means of an adaptive genetic algorithm (GA). To search for optimal antenna configurations, the present method employs a GA with an adaptive method that adjusts the characteristics of its selection, crossover, and mutation operators in order to maintain a diverse set of high-quality candidate solutions during its execution. It is shown that the present method can find an optimal solution faster than the conventional GA. Moreover, the fitness values of the optimal solutions obtained by the present method are better than those obtained by conventional GA.

关键词： adaptive genetic algorithm meander line antenna (MLA) moment method optimization

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