Fast multi-objective optimization of multi-parameter antenna structures based on improved MOEA/D with surrogate-assisted model |
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| Affiliation: | 1. School of Information Science and Engineering, Central South University, Changsha 410083, China;2. School of Physics and Electronics, Central South University, Changsha 410083, China;1. Aeronautical Key Laboratory for Digital Manufacturing Technologies, AVIC Beijing Aeronautical Manufacturing Technology Research Institute, Baliqiao, Chaoyang District, Beijing 100024, China;2. Key Laboratory of Inertial Science and Technology for National Defence, School of Instrument Science and Opto-Electronics Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China;1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China;2. Air Control and Navigation Institution, Air Force Engineering University, Xian 710000, China;1. Department of Communication Engineering, Xiamen University, Xiamen, Fujian, China;2. State Key Laboratory of Integrated Service Networks, Institute of Information Science, Xidian University, Xi’an, China |
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| Abstract: | For multi-objective design of multi-parameter antenna structures, optimization efficiency and computational cost are two major concerns. In this paper, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to improve global optimization capability by diversity detection operation and mixed population update operation. Further, in order to reduce the computational cost, a hybrid optimization strategy integrating a dynamically updatable surrogate-assisted model into the improved MOEA/D is proposed. The numerical results of test functions show that our algorithm outperforms original MOEA/D, modified MOEA/D (M-MOEA/D), and nondominated sorting genetic algorithm II (NGSA-II) in terms of diversity. Experimental validation of Pareto-optimal planar miniaturized multiband antenna designs is also provided, showing excellent convergence and considerable computational savings compared to those previously published approaches. |
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| Keywords: | Multi-objective optimization Antenna design MOEA/D Surrogate model |
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