改进鲸鱼优化算法在电力负荷调度中的应用

为了提高鲸鱼优化算法(WOA)的全局优化性能, 提出了一种基于黄金分割搜索的改进鲸鱼优化算法(GWOA)。首先利用黄金分割搜索对WOA的初始种群进行初始化, 使得初始种群能够尽可能的靠近全局最优解, 然后利用黄金分割搜索所形成的变区间, 进行变区间黄金分割非均匀变异操作, 以增加WOA的粒子多样性和提高粒子跳出局部最优陷阱的能力, 从而改善WOA的寻优性能。选取了15个大规模测试函数进行数值仿真测试, 仿真结果和统计分析表明GWOA的寻优性能要优于对比文献的改进鲸鱼优化算法(IWOA)。此外, 将GWOA用于对工程实际应用领域中的电力负荷优化调度问题进行实例分析, 实例应用结果表明, GWOA能有效对电力负荷优化调度问题进行寻优求解。

The Application of Improved Whale Optimization Algorithm in Power Load Dispatching

To improve global optimization performance of the whale optimization algorithm (WOA), an improved WOA based on the golden section search algorithm, called GWOA, is proposed. Firstly, the golden section algorithm is adopted in the algorithm to initiate the individuals’ position so as to enable the possibility of the initial population close to the global optimal solution. Secondly, the variable interval, which is formed by the process of golden section search, is introduced for non-uniform mutation operation, and this method improves the capacity of jumping out local optimum trap and enhances the diversity of particle. The proposed algorithm is first applied to 15 test functions and the experimental results show that the proposed algorithm has considerably improvement in solution precision and optimum efficiency and so on, and then, the simulation results of the power optimal load dispatch problems also show that the GWOA has good convergence property and better in quality of solution than other algorithms reported in recent literature.