改进的免疫遗传算法在无功优化应用中的研究

Application of Improved Immune Genetic Algorithm to Reactive Power Optimization

无功优化是电力网络优化的主要措施,其实质是一个多目标非线性混和优化问题。采用免疫遗传算法来研究该问题的求解方法就是在传统遗传算法的基础上,借鉴生物免疫机制中抗体的多样性保持策略和记忆抗原的特点,大大提高了算法的全局搜索和局部搜索能力。实验表明,免疫遗传算法具有很好的全局收敛性,能有效解决无功优化问题。

Reactive power optimization is a main measure of power grid optim a multi-object and nonlinear mixed optimization problem. This paper adopts ization and its nature is immune genetic algorithm,IGA, to study the solving process of this problem. Based on the traditional genetic algorithm, the immune genetic algorithm draws lessons from biotic diversity-maintenance and memory in antibody of immune mechanism, and improves the overall and local searching ability. The experiment shows that the IGA has good overall astringency and can effectively solve the reactive power optimization problem.