An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis
Antenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade‐offs that affect each other. In this paper, we formulate a multio...
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Published in | International journal of numerical modelling Vol. 35; no. 1 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Inc
01.01.2022
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Antenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade‐offs that affect each other. In this paper, we formulate a multiobjective beam pattern optimization problem (MBPOP) to simultaneously reduce the maximum sidelobe level (SLL) and achieve the nulls of the antenna array beam pattern. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a general and effective algorithm to solve the MOPs. However, it may be easy to lose population diversity and converge to local optimum. To overcome the issues above, we propose an improved MOEA/D (IMOEA/D) to deal with the formulated MBPOP. IMOEA/D introduces the normal distribution crossover operator (NDX), Lévy flight strategy and Euclidean distance‐based solution selection mechanism to enhance the performance of conventional MOEA/D to make it more suitable to solve the formulated MBPOP. Experiments are conducted and the results indicate that the proposed IMOEA/D has a better performance in terms of the convergence rate and population diversity compared to other algorithms for solving the formulated MBPOP. |
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Bibliography: | Funding information 13th Five‐year Plan Scientific Research Planning Project of Education Department of Jilin Province, Grant/Award Number: JJKH20200996KJ; Basic Scientific Research Expenses of Central Universities of China; Graduate Innovation Fund of Jilin University, Grant/Award Numbers: 101832020CX176, 101832020CX177; National Natural Science Foundation of China, Grant/Award Numbers: 62002133, 61872158, 61806083; Science and Technology Development Plan Project of Jilin Province, Grant/Award Numbers: 20190701019GH, 20190701002GH; Young Science and Technology Talent Lift Project of Jilin Province, Grant/Award Number: QT202013 |
ISSN: | 0894-3370 1099-1204 |
DOI: | 10.1002/jnm.2935 |