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  • Open Access


    Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

    Qinhui Liu1, Nengjian Wang1,*, Jiang Li1, Tongtong Ma2, Fapeng Li1, Zhijie Gao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2073-2091, 2023, DOI:10.32604/cmes.2022.021433

    Abstract As a typical transportation tool in the intelligent manufacturing system, Automatic Guided Vehicle (AGV) plays an indispensable role in the automatic production process of the workshop. Therefore, integrating AGV resources into production scheduling has become a research hotspot. For the scheduling problem of the flexible job shop adopting segmented AGV, a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function, and an improved genetic algorithm is designed to solve the problem in this study. The algorithm designs a two-layer coding method based on process coding and machine… More > Graphic Abstract

    Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

  • Open Access


    Optimization of Charging/Battery-Swap Station Location of Electric Vehicles with an Improved Genetic Algorithm-Based Model

    Bida Zhang1,*, Qiang Yan1, Hairui Zhang2, Lin Zhang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1177-1194, 2023, DOI:10.32604/cmes.2022.022089

    Abstract The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem. Considering the differences between these two modes of power replenishment, we constructed a joint location-planning model to minimize construction and operation costs, user costs, and user satisfaction-related penalty costs. We designed an improved genetic algorithm that changes the crossover rate using the fitness value, memorizes, and transfers excellent genes. In addition, the present model addresses the problem of “premature convergence” in conventional genetic algorithms. A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels… More >

  • Open Access


    An Improved Genetic Algorithm for Berth Scheduling at Bulk Terminal

    Xiaona Hu1,2, Shan Ji3, Hao Hua4, Baiqing Zhou1,*, Gang Hu5

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1285-1296, 2022, DOI:10.32604/csse.2022.029230

    Abstract Berth and loading and unloading machinery are not only the main factors that affecting the terminal operation, but also the main starting point of energy saving and emission reduction. In this paper, a genetic Algorithm Framework is designed for the berth allocation with low carbon and high efficiency at bulk terminal. In solving the problem, the scheduler’s experience is transformed into a regular way to obtain the initial solution. The individual is represented as a chromosome, and the sub-chromosomes are encoded as integers, the roulette wheel method is used for selection, the two-point crossing method is used for cross, and… More >

  • Open Access


    A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

    S. Velmurugan1,*, P. Ezhumalai2, E. A. Mary Anita3

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1093-1108, 2022, DOI:10.32604/iasc.2022.026308

    Abstract Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy… More >

  • Open Access


    Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

    Usra Yasmeen1, Muhammad Attique Khan1, Usman Tariq2, Junaid Ali Khan1, Muhammad Asfand E. Yar3, Ch. Avais Hanif4, Senghour Mey5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3667-3684, 2022, DOI:10.32604/cmc.2022.022264

    Abstract Agriculture is the backbone of each country, and almost 50% of the population is directly involved in farming. In Pakistan, several kinds of fruits are produced and exported the other countries. Citrus is an important fruit, and its production in Pakistan is higher than the other fruits. However, the diseases of citrus fruits such as canker, citrus scab, blight, and a few more impact the quality and quantity of this Fruit. The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure. In the agriculture sector, deep learning showing significant success in the… More >

  • Open Access


    An Improved Genetic Algorithm for Automated Convolutional Neural Network Design

    Rahul Dubey*, Jitendra Agrawal

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 747-763, 2022, DOI:10.32604/iasc.2022.020975

    Abstract Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily… More >

  • Open Access


    Case Optimization Using Improved Genetic Algorithm for Industrial Fuzzing Test

    Ming Wan1, Shiyan Zhang1, Yan Song2, Jiangyuan Yao3,*, Hao Luo1, Xingcan Cao4

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 857-871, 2021, DOI:10.32604/iasc.2021.017214

    Abstract Due to the lack of security consideration in the original design of industrial communication protocols, industrial fuzzing test which can successfully exploit various potential security vulnerabilities has become one new research hotspot. However, one critical issue is how to improve its testing efficiency. From this point of view, this paper proposes a novel fuzzing test case optimization approach based on improved genetic algorithm for industrial communication protocols. Moreover, a new individual selection strategy is designed as the selection operator in this genetic algorithm, which can be actively engaged in the fuzzing test case optimization process. In this individual selection strategy,… More >

  • Open Access


    Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

    Xiaokan Wang*, Qiong Wang

    Journal on Internet of Things, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jiot.2021.010228

    Abstract A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve. In the train control system, the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme, and the initial population can be formed by the way. The fitness function can be designed by the design requirements of the train control stop error, time error and energy consumption. the effectiveness of new individual can be ensured by checking the validity of the original individual when… More >

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