In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MED...In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MEDO,and achieves the automatic adjustment of the parameters.The proposed method is named as adaptive Maxwell’s equations derived optimization(AMEDO).In order to evaluate the performance of AMEDO,eight benchmarks are used and the results are compared with the original MEDO method.The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters,and at the same time can keep the accuracy and stability.Moreover,the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters.In the end,the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array,and shows great potential in antenna array synthesis.展开更多
A planar array antenna with arbitrary geometry synthesis technique based on genetic algorithm is discussed. This approach avoids coding/decoding and directly works with complex numbers to simplify computing program an...A planar array antenna with arbitrary geometry synthesis technique based on genetic algorithm is discussed. This approach avoids coding/decoding and directly works with complex numbers to simplify computing program and to speed up computation. This approach uses two crossover operators that can over-come premature convergence and the dependence of convergence on initial population. Simulation results show that this method is capable of synthesizing complex pattern shapes of planar arrays with arbitrary ge-ometry and can realize good sidelobe suppression at the same time.展开更多
基金the National Nature Science Foundation of China(No.61427803).
文摘In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MEDO,and achieves the automatic adjustment of the parameters.The proposed method is named as adaptive Maxwell’s equations derived optimization(AMEDO).In order to evaluate the performance of AMEDO,eight benchmarks are used and the results are compared with the original MEDO method.The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters,and at the same time can keep the accuracy and stability.Moreover,the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters.In the end,the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array,and shows great potential in antenna array synthesis.
文摘A planar array antenna with arbitrary geometry synthesis technique based on genetic algorithm is discussed. This approach avoids coding/decoding and directly works with complex numbers to simplify computing program and to speed up computation. This approach uses two crossover operators that can over-come premature convergence and the dependence of convergence on initial population. Simulation results show that this method is capable of synthesizing complex pattern shapes of planar arrays with arbitrary ge-ometry and can realize good sidelobe suppression at the same time.
