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An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem

机译:一种改进的人工蜂群算法(EABC)用于解决水热系统(DHTS)的调度问题

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摘要

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.
机译:水热系统调度是一个具有多重约束和高维的非线性规划问题,该模型的求解技术一直是研究的热点。基于人工蜂群算法能够有效解决高维问题的优点,提出了一种改进的人工蜂群算法来解决DHTS问题。该算法的改进包括两个方面。一方面,可以通过全局最优解的信息及其每一代的梯度来指导局部搜索的效率。全局最优解提高了算法的搜索效率,但失去了多样性,而梯度可以减弱由全局最优解引起的多样性的损失。另一方面,在遗传算法的启发下,通过选择,交叉和突变将极限生成中未更新的花蜜资源转化为新的花蜜资源,可以确保个体多样性并充分利用先验信息来改善该算法的全局搜索能力。最后通过经典的数字例子证明了ABC算法的两个改进是有效的。其中,遗传算子对于提高ABC算法的性能非常重要。将结果与其他最新算法进行了比较,增强的ABC算法在最小成本,平均成本和最大成本方面具有一般优势,这显示了其可用性和有效性。本文的研究成果为解决DHTS问题提供了一种新方法,也为改进机制和算法应用提供了新的参考。

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