Abstract:

Aiming at the shortcomings of the basic Moth-Flame Optimization（MFO） algorithm, such as slow convergence speed and low convergence precision, an improved moth optimization algorithm with Differential Evolution（DEMFO） is proposed. The algorithm first integrates the differential evolution algorithm into the MFO algorithm, which makes the moth population have the mechanism of variation, crossover and selection, while the DEMFO algorithm has stronger global and local search ability at the same time. It uses the Cauchy mutation operator to update the optimal position of the moth to produce a new solution, to maintain the diversity of the moth population, to help the algorithm jump out of the local optimum. Then the dynamic adaptive weight factor is introduced to make the moth update more flexible and guide the algorithm to the correct search direction, thus effectively improving the convergence and accuracy of the algorithm. The simulation experiment of the algorithm is also carried out with eight functions. From the experimental results, it can be seen that the improved moth optimization algorithm has a significant improvement in convergence speed and convergence accuracy. Finally, it is successfully applied to solve the Economic Dispatch（ED） model, and 140 unit examples are simulated on the Matlab platform. Compared with the basic MFO algorithm, the proposed DEMFO algorithm can obtain a higher quality optimization solution and provide a better load economic scheduling scheme, thus effectively reducing the power generation cost and generating huge economic benefits.

Key words: DEMFO algorithm, Differential Evolution（DE） algorithm, Cauchy mutation operator, dynamic adaptive weight factor, Economic Dispatch（ED）

摘要：

针对基本MFO算法存在后期收敛速度较慢、收敛精度低等缺点，提出了一种基于差分进化的改进飞蛾优化算法（DEMFO）。该算法首先将差分进化算法融合到MFO算法中，使得飞蛾种群个体之间具有变异、交叉、选择机制，DEMFO算法拥有更强的全局和局部搜索能力；运用柯西变异算子对飞蛾最优位置进行变异更新产生新解，保持飞蛾种群的多样性，帮助算法跳出局部最优；再引入动态自适应权重因子，使飞蛾的更新方式更具灵活性，引导算法朝着正确的搜索方向进行，从而有效地提高了算法的收敛性和精度；对该算法用8个测试函数进行仿真实验，从实验结果可以看出DEMFO算法在收敛速度和收敛精度上有了显著提高。将该算法成功应用于求解电力系统负荷经济调度（Economic Dispatch，ED）模型，在Matlab平台对140台机组算例进行了仿真，相比基本MFO算法，提出的DEMFO算法能够获得更高质量的优化解，提供更好的负荷经济调度方案，从而有效降低发电成本，产生巨大的经济效益。

关键词: DEMFO算法, 差分进化算法, 柯西变异算子, 动态自适应权重因子, 电力系统负荷经济调度