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| 一种基于Blind Kriging模型和差分进化的电磁结构优化算法* |
| A Hybrid Algorithm of Blind Kriging and Differential Evolution for Electromagnetic Structure Optimization |
| 投稿时间：2016-07-15 修订日期：2016-09-12 |
| DOI： |
| 中文关键词: 电磁结构 优化算法 Blind Kriging 差分进化 |
| 英文关键词:Electromagnetic structure Optimization method blind kriging differential evolution. |
| 基金项目:安徽省高等教育提升计划项目(TSKJ2014B05) |
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| 中文摘要: |
| 各类电磁结构日趋复杂, 设计自由度不断提升. 传统优化算法需要对大量的参数组合进行全波仿真试探, 设计效率普遍较低. 针对这一问题, 该文提出Blind Kriging (BK) 模型和差分进化相结合的电磁结构优化算法. 相比普通Kriging模型, BK模型通过贝叶斯参数选择算法将影响性能的主要因子加入回归模型，提高对响应的预测精度; 依据BK模型的预测结果从每代差分进化种群中选择最优个体执行电磁仿真. 由于优化过程中大量的电磁计算转移到快速的BK模型, 优化效率得到显著提升. 通过一个圆波导多螺钉极化转换器的优化设计, 表明该方法的求解质量和收敛速度优于现有算法. |
| 英文摘要: |
| Various electromagnetic(EM) structures become more complex and often have increasing degrees of design freedom. Classical optimization methods require numerous simulation trials of different parameter combinations, which leads to a low design efficiency. To address this problem, a hybrid EM structure optimization algorithm which combines blind kriging(BK) model with differential evolution(DE) is proposed in this paper. Comparing with ordinary kriging model, the prediction accuracy can be improved by adding main factors which are identified by Bayesian variable selection technique to the regression model. According to the predicted responses by BK models, the optimal individual is selected from every DE generation and reevaluated by EM simulation. Since most EM computation burden is shifted to efficient BK models, the design efficiency can be significantly improved. The proposed algorithm is validated by the optimization of a multi screw polarization converter, and it outperforms other existing algorithms in the quality of the solution and the convergence rate. |
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