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

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.水火电力系统调度属于带多约束、高维度的非线性规划问题，其模型求解方法一直是研究热点。鉴于人工蜂群算法（Artificial Bee Colony Algorithm, ABC）可高效求解高维问题的特性，本文提出一种改进人工蜂群算法以求解水火电力系统调度（DHTS）问题。该算法的改进包含两个方面：其一，借助每一代全局最优解及其梯度信息，可高效引导局部搜索。全局最优解虽能提升算法搜索效率，但会降低种群多样性，而梯度信息可削弱该全局最优解带来的多样性损失。其二，受遗传算法（Genetic Algorithm）启发，对连续多代未更新的蜜源，采用选择、交叉与变异操作生成新蜜源，既可保障个体多样性，又能充分利用先验信息，提升算法的全局搜索能力。最后，通过经典数值算例验证了ABC算法的两项改进均有效，其中用于提升ABC算法性能的遗传算子效果尤为显著。此外，将本文结果与其他前沿算法的结果进行对比，改进后的ABC算法在最小成本、平均成本与最大成本指标上均具备综合优势，验证了其可用性与有效性。本文研究成果为求解DHTS问题提供了新方法，同时也为算法机制改进与工程应用提供了全新参考。