A hybrid PSO–FPA metaheuristic algorithm for ultra-low sidelobe and high-directivity synthesis of concentric circular antenna arrays for advanced radar applications

This paper introduces a novel hybrid PSO–FPA metaheuristic algorithm that integrates the global exploration capability of the Flower Pollination Algorithm (FPA) with the adaptive convergence and dynamic search behavior of Particle Swarm Optimization (PSO) for the efficient synthesis of Concentric Circular Antenna Arrays (CCAAs). By embedding PSO’s inertia-weighted velocity update and acceleration coefficients into FPA’s global and local pollination phases, the proposed approach establishes a self-adaptive optimization framework capable of achieving an effective trade-off between global exploration and local exploitation. The algorithm is applied to the simultaneous optimization of excitation amplitudes and ring radii under four design configurations, considering both with and without central element scenarios. The optimization objective focuses on minimizing Side Lobe Levels (SLL) while maintaining high directivity and narrow Half-Power Beamwidth (HPBW). Comprehensive numerical simulations demonstrate that the proposed hybrid PSO–FPA algorithm outperforms conventional metaheuristics—including FPA, PSO, Artificial Bee Colony (ABC), and Whale Optimization Algorithm (WOA)—in terms of sidelobe suppression, convergence speed, and pattern symmetry. The hybrid method achieves a minimum SLL of − 45.01 dB, representing an improvement of approximately 38–42% over traditional techniques, and enhances beam symmetry and directivity by 24–28%, achieving up to 13.14 dB of main-lobe gain with minimal beamwidth degradation. Moreover, the joint optimization of amplitudes and ring radii yields a balanced radiation performance, characterized by focused beams with sidelobes below − 45 dB and computation times under 12 s per design. The results confirm that the proposed PSO–FPA metaheuristic delivers superior sidelobe suppression, enhanced beam control, and rapid convergence, making it a robust and scalable optimization tool for next-generation antenna synthesis in radar, wireless communication, and smart sensing systems requiring precise directional control and interference mitigation.

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Publication Details

Journal
Scientific Reports
Published
2026-02-03
DOI
https://doi.org/10.1038/s41598-026-36315-6
Primary Topic
Antenna Design and Optimization
Type
article
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article

A hybrid PSO–FPA metaheuristic algorithm for ultra-low sidelobe and high-directivity synthesis of concentric circular antenna arrays for advanced radar applications

Ayodeji Olalekan Salau, Mohammed El Brahimi, Omar Almomani, Imane Haouam et al.
Scientific Reports
Antenna Design and Optimization
article

A hybrid PSO–FPA metaheuristic algorithm for ultra-low sidelobe and high-directivity synthesis of concentric circular antenna arrays for advanced radar applications

Ayodeji Olalekan Salau, Mohammed El Brahimi, Omar Almomani, Imane Haouam, Iryna Hunko, Riyadh Bouddou
article en

Abstract

This paper introduces a novel hybrid PSO–FPA metaheuristic algorithm that integrates the global exploration capability of the Flower Pollination Algorithm (FPA) with the adaptive convergence and dynamic search behavior of Particle Swarm Optimization (PSO) for the efficient synthesis of Concentric Circular Antenna Arrays (CCAAs). By embedding PSO’s inertia-weighted velocity update and acceleration coefficients into FPA’s global and local pollination phases, the proposed approach establishes a self-adaptive optimization framework capable of achieving an effective trade-off between global exploration and local exploitation. The algorithm is applied to the simultaneous optimization of excitation amplitudes and ring radii under four design configurations, considering both with and without central element scenarios. The optimization objective focuses on minimizing Side Lobe Levels (SLL) while maintaining high directivity and narrow Half-Power Beamwidth (HPBW). Comprehensive numerical simulations demonstrate that the proposed hybrid PSO–FPA algorithm outperforms conventional metaheuristics—including FPA, PSO, Artificial Bee Colony (ABC), and Whale Optimization Algorithm (WOA)—in terms of sidelobe suppression, convergence speed, and pattern symmetry. The hybrid method achieves a minimum SLL of − 45.01 dB, representing an improvement of approximately 38–42% over traditional techniques, and enhances beam symmetry and directivity by 24–28%, achieving up to 13.14 dB of main-lobe gain with minimal beamwidth degradation. Moreover, the joint optimization of amplitudes and ring radii yields a balanced radiation performance, characterized by focused beams with sidelobes below − 45 dB and computation times under 12 s per design. The results confirm that the proposed PSO–FPA metaheuristic delivers superior sidelobe suppression, enhanced beam control, and rapid convergence, making it a robust and scalable optimization tool for next-generation antenna synthesis in radar, wireless communication, and smart sensing systems requiring precise directional control and interference mitigation.

Scientific ReportsVol. 16(1)
Al-Ahliyya Amman University (JO), Northwest African American Museum (US), Afe Babalola University (NG), Vinnytsia National Technical University (UA), Saveetha University (IN)
Openalex Percentile: Top 69%
Antenna Design and Optimization
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