本文選題：電磁結構 + 優(yōu)化算法�。� 參考：《電波科學學報》2017年03期

【摘要】：進化算法在各類電磁結構優(yōu)化設計中有著廣泛的應用,但由于需要在參數(shù)空間中進行隨機搜索并仿真試探,優(yōu)化效率普遍較低.針對這一問題,提出受限差分進化(Differential Evolution,DE)算法與Kriging代理模型相結合的電磁結構快速優(yōu)化算法.算法根據參考設計結果建立圓柱管道空間,通過參數(shù)變換將進化區(qū)域限制在管道內部.Kriging模型學習管道內樣本及其仿真數(shù)據,代替電磁仿真快速預測進化產生下一代種群的響應.相比整個參數(shù)空間,該算法DE尋優(yōu)和Kriging學習的區(qū)域被顯著減小,優(yōu)化效率得到提升.通過一個波導雙孔定向耦合器的優(yōu)化設計,表明該方法的求解質量和收斂速度優(yōu)于現(xiàn)有算法.
[Abstract]:The evolutionary algorithm is widely used in the optimization design of various electromagnetic structures. However, due to the need for random search and Simulation in the parameter space, the optimization efficiency is generally low. In view of this problem, a fast optimization calculation of the electromagnetic structure combined with the Differential Evolution (DE) algorithm and the Kriging agent model is proposed. The algorithm establishes a cylindrical pipe space based on the reference design results. Through the parameter transformation, the evolutionary region is restricted to the.Kriging model inside the pipeline to learn the sample and its simulation data, instead of the electromagnetic simulation to quickly predict the response of the next generation of the population. Compared with the whole parameter space, the algorithm DE is optimized and the Kriging learning area is found. The optimal design of a waveguide double hole directional coupler shows that the solution quality and convergence speed of the method are better than those of the existing algorithms.

【作者單位】： 安徽工程大學電氣工程學院通信工程系;
【基金】：安徽省高等教育提升計劃項目(TSKJ2014B05,TSKJ2015B19)
【分類號】：TN622;TP18

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