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

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）