遗传算法和神经网络在组合优化中的应用

Shiqiong Zhou, L. Kang, Guifang Guo, Yanning Zhang, Jianbo Cao, Bing-gang Cao
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引用次数: 14

摘要

建立了可再生能源发电系统储层优化模型,采用遗传算法和遗传算法与神经网络组合优化两种方法求解该模型。该系统包括光伏阵列、铅酸电池和飞轮。最优规模可以看作是一个约束优化问题:以失电概率(LPSP)为主要约束,使储能系统的总容量最小化。两种优化算法均取得了较好的效果。我们可以看到,遗传算法和神经网络组合优化可以减少计算时间,结果变化不大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The application of combinatorial optimization by Genetic Algorithm and Neural Network
A optimization model of sizing the storage section in a renewable power generation system was set up, and two methods were used to solve the model: genetic algorithm or combinatorial optimization by genetic algorithm and neural network. The system includes the photovoltaic arrays, the lead-acid battery and a flywheel. The optimal sizing can be considered as a constrained optimization problem: minimization the total capacity of energy storage system, subject to the main constraint of the loss of power supply probability (LPSP). Both of the two optimal algorithm got good results. We can see that, combinatorial optimization by genetic algorithm and neural network can lessen the calculation time, with the results change little.
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