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提出了一种实用混合策略来求解无功优化工程应用问题。该方法结合了遗传算法(GA)和免疫算法(IA)建立具有动态调整的罚因子的目标函数,在遗传操作中,引入人工免疫机制,以保存种群的多样性同时保证算法能够较快的收敛。同时,本文针对一般遗传算法交叉变异概率选择定值情形,引入了自适应改进策略,避免了通常情况下经验性的缺陷。通过IEEE30节点系统的仿真计算,将本文混合策略与其它的算法进行了比较,结果表明本文混合策略在计算速度和优化效果方面都具有明显的优势。
A practical hybrid strategy is proposed to solve the problem of reactive power optimization engineering application. This method combines genetic algorithm (GA) and immune algorithm (IA) to establish objective function with dynamically adjusted penalty factor. In the genetic operation, artificial immune mechanism is introduced to save the diversity of population while ensuring the algorithm converges faster . At the same time, in this paper, aiming at the selection of crossover mutation probabilities of general genetic algorithm, an adaptive improvement strategy is introduced, which avoids the usual empirical drawbacks. Through the simulation of IEEE30 node system, the hybrid strategy in this paper is compared with other algorithms. The results show that the hybrid strategy in this paper has obvious advantages in terms of computing speed and optimization effect.