Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerica...
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Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerical, and metaheuristic algorithms aimed at this task in recent years, the extraction of parameters remains a formidable obstacle. This study employs the Grey Wolf Optimizer (GWO) to extract the five key parameters of the RTC France solar cell. The GWO’s performance is systematically compared with metaheuristic algorithms such as Enhanced Chaotic JAYA (CJAYA) and Performance-Guided JAYA (PGJAYA). The study showcases the prowess of GWO in optimizing PV parameters, marking a significant stride forward in the realm of optimization techniques for PV cell modeling. Through meticulous analysis using MATLAB-SIMULINK, the research unveils the profound effectiveness of GWO in navigating the intricate landscape of parameter extraction within PV systems.
Smart grid systems (SGSs) are leading the modernization of energy infrastructure by integrating advanced 10 technologies to improve efficiency, reliability, and sustainability. These systems demand sophisticated tools...
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Smart grid systems (SGSs) are leading the modernization of energy infrastructure by integrating advanced 10 technologies to improve efficiency, reliability, and sustainability. These systems demand sophisticated tools 11 to address their complexity, with forecasting and optimization being crucial areas of focus. Machine learning 12 (ML) techniques, including both traditional neural networks and advanced deep learning approaches, play 13 a significant role in tackling the intricate challenges of SGSs. These methods enable accurate forecasting, 14 which is essential for predicting electricity demand, renewable energy generation, and system loads. By 15 supporting informed decision-making and efficient resource allocation, ML provides both theoretical 16 contributions and practical applications for smart grids. 17 This special issue, Data-Driven Approaches for Efficient Smart Grid Systems, explores the innovative use 18 of machine learning to address challenges specific to SGSs. Forecasting is central to these efforts, as it forms 19 the basis for understanding and managing the complex dynamics of SGSs. While traditional methods have 20 demonstrated promise, they also highlight limitations in adaptability, scalability, and precision, particularly 21 when addressing the evolving needs of modern smart grids. These challenges call for advanced algorithms 22 that integrate diverse data sources, capture spatiotemporal relationships, and account for *** special issue is organized into four thematic areas ("forecasting and prediction techniques","optimization and scheduling in power systems", "data quality, validation, and identification", and "research 25 trends and evaluations in energy systems"), which highlight the variety of approaches and contributions 26 from the 13 papers *** first area focuses on forecasting and prediction techniques, essential for managing renewable energy
Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issu...
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Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.
The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbala...
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The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To overcome these drawbacks, an improved SMA with a dynamic quantum rotation gate and opposition-based learning (DQOBLSMA) is proposed in this paper. Specifically, for the first time, two mechanisms are used simultaneously to improve the robustness of the original SMA: the dynamic quantum rotation gate and opposition-based learning. The dynamic quantum rotation gate proposes an adaptive parameter control strategy based on the fitness to achieve a balance between exploitation and exploration compared to the original quantum rotation gate. The opposition-based learning strategy enhances population diversity and avoids falling into the local optima. Twenty-three benchmark test functions verify the superiority of the DQOBLSMA. Three typical engineering design problems demonstrate the ability of the DQOBLSMA to solve practical problems. Experimental results show that the proposed algorithm outperforms other comparative algorithms in convergence speed, convergence accuracy, and reliability.
In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) ha...
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ISBN:
(纸本)9780956715777
In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) have been applied in conjunction with Least Square Support Vector Machine (LS-SVM) approach to optimize the total misclassification error in term of False Positive and False Negative rates. We validate our experimental results, based on five well known unbalanced medical datasets. Presented results show that the SA achieved the best results. Both SA and GA outperform PSO metaheuristic.
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