Efficient multi-objective synthesis for microwave components based on computational intelligence techniques

Multi-objective synthesis for microwave components (e.g. integrated transformer, antenna) is in high demand. Since the embedded electromagnetic (EM) simulations make these tasks very computationally expensive when using traditional multi-objective synthesis methods, efficiency improvement is very important. However, this research is almost blank. In this paper, a new method, called Gaussian Process assisted multi-objective optimization with generation control (GPMOOG), is proposed. GPMOOG uses MOEA/D-DE as the multi-objective optimizer, and a Gaussian Process surrogate model is constructed ON-LINE to predict the results of expensive EM simulations. To avoid false optima for the on-line surrogate model assisted evolutionary computation, a generation control method is used. GPMOOG is demonstrated by a 60GHz integrated transformer, a 1.6GHz antenna and mathematical benchmark problems. Experiments show that compared to directly using a multi-objective evolutionary algorithm in combination with an EM simulator, which is the best known method in terms of solution quality, comparable results can be obtained by GPMOOG, but at about 1/3-1/4 of the computational effort.status: publishe

Efficient Multi-objective Synthesis for Microwave Components Based on Computational Intelligence Techniques

Multi-objective synthesis for microwave components (e.g. integrated transformer, antenna) is in high demand. Since the embedded electromagnetic (EM) simulations make these tasks very computationally expensive when using traditional multi-objective synthesis methods, efficiency improvement is very important. However, this research is almost blank. In this paper, a new method, called Gaussian Process assisted multi-objective optimization with generation control (GPMOOG), is proposed. GPMOOG uses MOEA/D-DE as the multi-objective optimizer, and a Gaussian Process surrogate model is constructed ON-LINE to predict the results of expensive EM simulations. To avoid false optima for the on-line surrogate model assisted evolutionary computation, a generation control method is used. GPMOOG is demonstrated by a 60GHz integrated transformer, a 1.6GHz antenna and mathematical benchmark problems. Experiments show that compared to directly using a multi-objective evolutionary algorithm in combination with an EM simulator, which is the best known method in terms of solution quality, comparable results can be obtained by GPMOOG, but at about 1/3-1/4 of the computational effort