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Multi-objective self-organizing optimization for constrained sparse array synthesis
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DOI：10.1016/j.swevo.2020.100743.png)
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Sparse span array is a critical communication technology for detecting microwave signal, yet it is difficult to simultaneously satisfy both reducing antenna elements' number and maintaining maximum side lobe level. Towards this problem, we propose a multi-objective optimization approach for self-organizing limited-area sparse span array, termed MOSSA. Overall, a uniform framework of multi-objective sparse span array is proposed. Specially, two objectives, number of selected antenna and peak side lobe level, are established for exploring the optimal array distribution in the framework. Based on the framework, for the problem of global-optimum array distribution, we propose a multi-objective particle swarm optimization searching pattern and design a MOSSA algorithm; Furthermore, for the problem of flexibly-adjusted self-organizing array structure, we present a multiobjective genetic programming searching pattern and design a MOSSA-gp algorithm. Moreover, a limited-region mode supplements to the framework. Finally, combination decision strategy assists users to screen out suitable solutions under the guidance of fuzzy-range indexes and then select the optimal solution by a triangle-approximating approach based on minimum Manhattan distance. Numerous experiments demonstrate that the proposed MOSSA outperforms other state-of-the-art algorithms in terms of both antenna elements' number and maximum side lobe level.
Keyword:
MOEAs
Genetic programming
Sparse plane array
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