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A Novel Multi-Strategy Nonlinear Multi-Objective Particle Swarm Optimization Algorithm for Sparse Planar Antenna Array Synthesis
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DOI：10.1109/tgcn.2026.3701739.png)
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Abstract
En 中文
Recent research on the optimization of sparse antenna arrays has concentrated on minimizing the sidelobe level (SLL), which often leads to an imbalanced performance in the overall radiation pattern synthesis. To address this issue, we present a novel multi-strategy nonlinear multi-objective particle swarm optimization (MSNL-MOPSO) algorithm based on global learning integration strategy and local mutation joint mechanism. First, a hybrid multi-objective optimization problem (MOP) model is constructed to jointly minimize the peak sidelobe level (PSLL) and beamwidth (BW), mitigating the imbalance problem encountered in radiation pattern synthesis. Then, a global learning integration method based on adaptive inertia weight and dandelion flight mechanism is designed to enhance population diversity and global search capabilities. Furthermore, a local mutation joint approach based on logarithmic spiral opposition-based learning (LSOBL) and Cauchy-golden sine inverse cumulative mutation (CGSICM) strategy is employed to improve convergence precision. Finally, experimental evaluations on four classical benchmark functions demonstrate the superior performance of the proposed MSNL-MOPSO algorithm. Additionally, the experimental results of two sparse planar antenna arrays synthesis show that MSNL-MOPSO achieves a 2.1 dB reduction in PSLL and a 20% improvement in convergence speed compared to mainstream algorithms (NSGA-II, MOGWO, MORIME, and MOPSO). These findings confirm the significant potential of MSNL-MOPSO for optimizing complex sparse planar antenna array configurations.
Keywords:
Multi-objective particle swarm optimization (MOPSO)
peak sidelobe level (PSLL)
beamwidth (BW)
sparse antenna array
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