The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges ...
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The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learningalgorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.
With the rapid development of vehicle-mounted communication technology, GPS data is an effective method to predict the current road vehicle track based on vehicle-mounted data. GPS-oriented vehicle-mounted data positi...
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With the rapid development of vehicle-mounted communication technology, GPS data is an effective method to predict the current road vehicle track based on vehicle-mounted data. GPS-oriented vehicle-mounted data position prediction method is currently a hot research work and an effective method to realize intelligent transportation. In this paper, an improvement scheme is proposed based on the problem of falling into local optimization existing in the basic algorithm of teaching and learning optimization algorithm. An interference operator is used to disturb teachers to enhance the kinetic energy of the population to jump out of local optimization. By comparing the performance of GA, PSO, TLBO, and ITLBO algorithms with four test functions, the results show that ITLBO has efficient optimization effect and generalization ability. Finally, the ITLBO-ELM algorithm has the best prediction effect by comparing the vehicle GPS data and comparing the experimental algorithms.
This paper studies the automatic generation control (AGC) problem of isolated two-area multiple source microgrid energies under the influence of stochasticity and volatility of renewable energies and loads. A Load Fre...
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ISBN:
(纸本)9781728159225
This paper studies the automatic generation control (AGC) problem of isolated two-area multiple source microgrid energies under the influence of stochasticity and volatility of renewable energies and loads. A Load Frequency Control (LFC) model of isolated two-area microgrid with multiple distributed energy resources and energy storage device is established. The wind power, the photovoltaic output and load fluctuation are formulated as discrete-time Markov processes based on their stochastic dynamic characteristics. The performance of the proposed learning optimization algorithm combined with the AGC principle of the power grid is demonstrated on microgrid system. At last, comparing SAQ-learning with traditional Q-learning and PI controller, simulation results illustrated the rationality of the proposed microgrid model and the expected performance of the controller.
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