Yu Yi, Wu Yonggang, Hu Binqi, Liu Xinglong
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, China.
Dispatching and Communication Bureau, State Grid Hunan Electric Power Company, Changsha, China.
PLoS One. 2018 Jan 11;13(1):e0189282. doi: 10.1371/journal.pone.0189282. eCollection 2018.
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.
水火电系统调度是一个具有多个约束条件和高维度的非线性规划问题,该模型的求解技术一直是研究热点。基于人工蜂群算法(ABC)能够有效解决高维问题的优势,本文提出了一种改进的人工蜂群算法来解决水火电系统调度问题。所提算法的改进包括两个方面。一方面,利用每一代全局最优解及其梯度信息高效引导局部搜索。全局最优解提高了算法的搜索效率,但损失了多样性,而梯度可以减弱全局最优解导致的多样性损失。另一方面,受遗传算法启发,对在限定代数内未更新的蜜源进行选择、交叉和变异操作,将其转化为新蜜源,这既能保证个体多样性,又能充分利用先验信息提高算法的全局搜索能力。最后通过一个经典算例证明了ABC算法的这两个改进是有效的。其中用于提升ABC算法性能的遗传算子作用显著。还将结果与其他现有先进算法的结果进行了比较,改进后的ABC算法在最小成本、平均成本和最大成本方面具有普遍优势,显示出其可用性和有效性。本文的研究成果为解决水火电系统调度问题提供了一种新方法,也为算法机理改进及算法应用提供了新的参考。