Global Optimization of Microwave Filters Based on a Surrogate Model Assisted Evolutionary Algorithm
Liu, Bo, Yang, Hao and Lancaster, Michael (2017) Global Optimization of Microwave Filters Based on a Surrogate Model Assisted Evolutionary Algorithm. IEEE Transactions on Microwave Theory and Techniques, 65 (6). pp. 1976-1985. ISSN 0018-9480
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Abstract
Local optimization is a routine approach for full-wave optimization of microwave filters. For filter optimization problems with numerous local optima or where the initial design is not near to the optimal region, the success rate of the routine method may not be high. Traditional global optimization techniques have a high success rate for such problems, but are often prohibitively computationally expensive considering the cost of full-wave electromagnetic simulations. To address the above challenge, a new method, called surrogate model-assisted evolutionary algorithm for filter optimization (SMEAFO), is proposed. In SMEAFO, considering the characteristics of filter design landscapes, Gaussian process surrogate modeling, differential evolution operators, and Gaussian local search are organized in a particular way to balance the exploration ability and the surrogate model quality, so as to obtain high-quality results in an efficient manner. The performance of SMEAFO is demonstrated by two real-world design cases (a waveguide filter and a microstrip filter), which do not appear to be solvable by popular local optimization techniques. Experiments show that SMEAFO obtains high-quality designs comparable with global optimization techniques but within a reasonable amount of time. Moreover, SMEAFO is not restricted by certain types of filters or responses. The SMEAFO-based filter design optimization tool can be downloaded from http://fde.cadescenter.com.
| Item Type: | Article |
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| Keywords: | Optimization, Computational modeling, Mathematical model, Statistics, Microwave filters |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Users 1048 not found. |
| Date Deposited: | 14 Aug 2017 10:49 |
| Last Modified: | 26 Apr 2018 14:52 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/16026 |
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