基于改进型NSGA-Ⅱ算法的深海高频变压器优化研究

The Deep-sea High-frequency Transformer Optimization Research Based on the Improved NSGA-Ⅱ Algorithm

针对深海高频变压器建立其体积和损耗的多目标优化数学模型。利用遗传算法在求解非线性多目标优化问题中的优势,解决深海高频变压器损耗目标和体积目标相互矛盾的问题。改进NSGA-Ⅱ算法中的非支配排序策略和选择截断策略,使二者处于同一非支配层,从而增加了周围密度小的个体的遗传概率,提高了算法的全局搜索能力和运行速度。运用改进型NSGA-Ⅱ算法对6 kW高频变压器进行优化设计并根据优化结果制造出高频变压器实体。试验测试结果表明,采用改进型NSGA-Ⅱ算法能有效地降低深海高频变压器的总损耗和体积。

Aimed at the deep-sea high-frequency transformer, the multi-objective optimization mathematical model of the volume and the depletion can be established. It deals with the optimization design of contradiction between loss and volume target of the deep-sea high- frequency transformer, taking the advantage of genetic algorithm into solving nonlinear multi- objective optimization problems. By changing and improving the non-dominated sorting and choosing truncation strategies in the NSGA-Ⅱ algorithm, individuals with low density in one non-dominated level can possess larger genetic probability, which improves the global search ability of the algorithm and increases its speed. The improved NSGA-Ⅱ algorithm was used to make optimization designs to 6 kW high-frequency transformer, and the high-frequency transformer entitycould be manufactured according to the results of optimization. The test proves that the improved NSGA -Ⅱ algorithm can effectively reduce the total loss and the volume of deep-sea high-frequency transformer.