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  • Open Access

    ARTICLE

    An Optimisation Strategy for Electric Vehicle Charging Station Layout Incorporating Mini Batch K-Means and Simulated Annealing Algorithms

    Haojie Yang, Xiang Wen, Peng Geng*

    Journal on Artificial Intelligence, Vol.6, pp. 283-300, 2024, DOI:10.32604/jai.2024.056303 - 18 October 2024

    Abstract To enhance the rationality of the layout of electric vehicle charging stations, meet the actual needs of users, and optimise the service range and coverage efficiency of charging stations, this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms. By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius, the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are… More >

  • Open Access

    ARTICLE

    Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

    Caixia Tao, Shize Yang*, Taiguo Li

    Energy Engineering, Vol.121, No.1, pp. 187-201, 2024, DOI:10.32604/ee.2023.042421 - 27 December 2023

    Abstract With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into… 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 - 26 December 2023

    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

    Sustainable Waste Collection Vehicle Routing Problem for COVID-19

    G. Niranjani1,*, K. Umamaheswari2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 457-472, 2022, DOI:10.32604/iasc.2022.024264 - 05 January 2022

    Abstract COVID-19 pandemic has imposed many threats. One among them is the accumulation of waste in hospitals. Waste should be disposed regularly and safely using sustainable methods. Sustainability is self development with preservation of society and its resources. The main objective of this research is to achieve sustainability in waste collection by minimizing the cost factor. Minimization of sustainable-cost involves minimization of three sub-components – total travel-cost representing economical component, total emission-cost representing environmental component and total driver-allowance-cost representing social component. Most papers under waste collection implement Tabu search algorithm and fail to consider the environmental More >

  • Open Access

    ARTICLE

    Yield Stress Prediction Model of RAFM Steel Based on the Improved GDM-SA-SVR Algorithm

    Sifan Long1, Ming Zhao2,*, Xinfu He3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 727-760, 2019, DOI:10.32604/cmc.2019.04454

    Abstract With the development of society and the exhaustion of fossil energy, researcher need to identify new alternative energy sources. Nuclear energy is a very good choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material reduced activation ferritic/martensitic (RAFM) steel. The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method (GDM) which combines the gradient… More >

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