Affiliations: [a] Department of EE, China Jiliang University, Hangzhou, China | [b] Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou, China
Correspondence:
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Corresponding author: Siguang An, College of EE, China Jiliang University, Hangzhou, Zhejiang, China. E-mail: [email protected]
Abstract: Due to the high requirements for performance of antennas in the modern communication system, more and more objectives need to be considered, which makes the antenna design unendurable time-consuming and hard to converge. To obtain the Pareto solutions for antenna design effectively and efficiently, a dual reduction method is incorporated in an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D). To obtain information about conflicting objectives on the Pareto front, the size of the MOEA/D neighbourhood is dynamically adjusted; to improve the speed and accuracy of finding redundant objectives, PCA and Pareto impact ratio are used as dual objective-reduction criteria. Numerical results in solving the mathematical benchmark problems, an antenna array problem and Yagi-uda optimal design demonstrate that the proposed method can reduce redundant objectives and increase the convergence speed.
