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a novel fitness function for utilization for images segmentation using a metaheuristic method (GA, pso, sfla, aco...)
Date : 2025-12-15 Size : 158kb User : harouni

This work investigates the practical application of support vector machine (SVM) to power transformer condition assessment. Partiuclarly, this paper proposes to integrate the SVM algorithm with two heuristic optimization algorithms which are particle swarm optimization algorithm (PSO) and genetic algorithm optimization (GA). These two optimization algorothms are used for efficiently and effectively determine the optimal parameters for SVM. The resulatant two hybrid algorithms, i.e. SVM-PSO and SVM-GA can improve the performances of the original SVM algorithm on classifying the incipient faults in power transformers. Extensive case studies and statistic comparison among the original SVM, SVM-PSO, and SVM-GA over multiple datasets are also provided. Calculation results may demonstrate the effectiveness and applic
Date : 2025-12-15 Size : 1.8mb User : pse

This work investigates the practical application of support vector machine (SVM) to power transformer condition assessment. Partiuclarly, this paper proposes to integrate the SVM algorithm with two heuristic optimization algorithms which are particle swarm optimization algorithm (PSO) and genetic algorithm optimization (GA). These two optimization algorothms are used for efficiently and effectively determine the optimal parameters for SVM. The resulatant two hybrid algorithms, i.e. SVM-PSO and SVM-GA can improve the performances of the original SVM algorithm on classifying the incipient faults in power transformers. Extensive case studies and statistic comparison among the original SVM, SVM-PSO, and SVM-GA over multiple datasets are also provided. Calculation results may demonstrate the effectiveness and applicability of the two hybrid algorit
Date : 2025-12-15 Size : 909kb User : pse

This work investigates the practical application of support vector machine (SVM) to power transformer condition assessment. Partiuclarly, this paper proposes to integrate the SVM algorithm with two heuristic optimization algorithms which are particle swarm optimization algorithm (PSO) and genetic algorithm optimization (GA). These two optimization algorothms are used for efficiently and effectively determine the optimal parameters for SVM. The resulatant two hybrid algorithms, i.e. SVM-PSO and SVM-GA can improve the performances of the original SVM algorithm on classifying the incipient faults in power transformers. Extensive case studies and statistic comparison among the original SVM, SVM-PSO, and SVM-GA over multiple datasets are also provided. Calculation results may demonstrate the effectiveness and applicability of the two hybrid algorithms in improving the classification accuracy of SVM for condition
Date : 2025-12-15 Size : 655kb User : pse

基于遗传算法的电力系统无功优化的C语言程序-Power system based on genetic algorithm optimization of reactive C language program
Date : 2025-12-15 Size : 12kb User : wanghan

ELD with GA and PSO based Solution published paper for your reference
Date : 2025-12-15 Size : 196kb User : Pravat

this code hybrid ga and pso is for theresholding
Date : 2025-12-15 Size : 7kb User : hadi

粒子群算法与遗传算法的联合的GA-PSO算法运用,带有测试函数-Joint GA-PSO algorithm using particle swarm optimization and genetic algorithm with test function
Date : 2025-12-15 Size : 12kb User : 张煜坤
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