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  • Open Access

    ARTICLE

    Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

    Zakir Hussain Ahmed1,*, Habibollah Haron2, Abdullah Al-Tameem3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2399-2425, 2024, DOI:10.32604/cmc.2024.049704

    Abstract Genetic algorithms (GAs) are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simple GA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators… More >

  • Open Access

    ARTICLE

    Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm

    Parth Khandelwal1, Harshit2, Indranil Manna1,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1727-1755, 2024, DOI:10.32604/cmc.2024.042752

    Abstract Metallic alloys for a given application are usually designed to achieve the desired properties by devising experiments based on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises. However, the influence of process parameters and material properties is often non-linear and non-colligative. In recent years, machine learning (ML) has emerged as a promising tool to deal with the complex interrelation between composition, properties, and process parameters to facilitate accelerated discovery and development of new alloys and functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles, to design… More > Graphic Abstract

    Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm

  • Open Access

    ARTICLE

    MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

    Rashmi Sharma1, Ashok Pal1, Nitin Mittal2, Lalit Kumar2, Sreypov Van3, Yunyoung Nam3,*, Mohamed Abouhawwash4,5

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3489-3510, 2024, DOI:10.32604/cmc.2024.046606

    Abstract This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm (MOALO) which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm (ALO) and the Genetic Algorithm (GA). MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions. The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO. A first-time hybrid of these… More >

  • Open Access

    ARTICLE

    Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

    Qinhui Liu, Laizheng Zhu, Zhijie Gao, Jilong Wang, Jiang Li*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 811-843, 2024, DOI:10.32604/cmc.2023.046040

    Abstract To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematical model is established to minimize the maximum completion time. Secondly, an improved two-layer optimization algorithm is designed: the outer layer algorithm uses an improved PSO (Particle Swarm Optimization) to solve the workpiece batching problem, and the inner layer algorithm uses an improved GA (Genetic Algorithm) to solve the dual-resource scheduling problem. Then, a rescheduling method… More >

  • Open Access

    ARTICLE

    A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

    Liang Zeng1,2, Junyang Shi1, Yanyan Li1, Shanshan Wang1,2,*, Weigang Li3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803

    Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III… More >

  • Open Access

    ARTICLE

    A Multi-Objective Genetic Algorithm Based Load Balancing Strategy for Health Monitoring Systems in Fog-Cloud

    Hayder Makki Shakir, Jaber Karimpour*, Jafar Razmara

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545

    Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource… More >

  • Open Access

    ARTICLE

    An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

    Jiamin Xiang1, Ying Zhang1, Xiaohua Cao1,*, Zhigang Zhou2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3443-3466, 2023, DOI:10.32604/cmc.2023.045120

    Abstract 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… More >

  • Open Access

    ARTICLE

    New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

    Shimaa M. Amer1, Ashraf A. M. Khalaf2, Amr H. Hussein3,4, Salman A. Alqahtani5, Mostafa H. Dahshan6, Hossam M. Kassem3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2749-2767, 2024, DOI:10.32604/cmes.2023.029138

    Abstract Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s… More >

  • Open Access

    ARTICLE

    Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based on Genetic Algorithm

    Jiahao Lu1, Fangfang Zhang1, Weilong Guang1, Yanzhao Wu1, Ran Tao1,2,*, Xiaoqin Li1,2, Ruofu Xiao1,2

    Energy Engineering, Vol.120, No.12, pp. 2819-2837, 2023, DOI:10.32604/ee.2023.042287

    Abstract Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions. Unlike conventional turbines, Savonius turbines employ S-shaped blades and have simple internal structures. Therefore, there is a large space for optimizing the blade geometry. In this study, computational fluid dynamics (CFD) numerical simulation and genetic algorithm (GA) were used for the optimal design. The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results. The weighted objective function was constructed with the maximum power coefficient Cp and the high-power coefficient range R under multiple… More >

  • Open Access

    ARTICLE

    Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

    Xiaobao Yu*, Kang Yang

    Energy Engineering, Vol.120, No.12, pp. 2741-2760, 2023, DOI:10.32604/ee.2023.028041

    Abstract Taking an industrial park as an example, this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources (DERs). The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data. To predict and analyze the load growth of the industrial park, an improved back-propagation algorithm is employed. Furthermore, the study classifies users within the industrial park according to their specific power consumption and supply requirements. This user segmentation allows for the introduction of three constraints: node voltage, wire current, and capacity More >

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