International Journal of Scientific Research and Engineering Development

Holographic massive multiple-input multiple-output (HMIMO) antenna systems are widely regarded as a foundational technology for sixth-generation (6G) wireless networks due to their ability to provide extreme spatial resolution, near-field beam focusing, and unprecedented spectral efficiency. Achieving these capabilities requires the synthesis of super-directive antenna arrays capable of generating highly focused electromagnetic beams. However, classical super-directive array designs often suffer from severe electromagnetic limitations, including excessive quality factor, narrow bandwidth, poor radiation efficiency, and impedance mismatch, rendering many theoretically optimal solutions practically unrealizable. This paper presents Evo-HMAA, a physics-aware hybrid multi-agent evolutionary optimization framework designed to address these challenges. Evo-HMAA integrates Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) within a cooperative architecture that enables information sharing among sub-populations. Electromagnetic-aware constraints are embedded directly into a multi-objective fitness formulation that simultaneously optimizes directivity, realized gain, sidelobe level, half-power beamwidth, and impedance matching. In addition, Gaussian Process surrogate modeling is employed to reduce computational complexity during early optimization stages. Extensive simulations conducted in MATLAB, with validation using full-wave electromagnetic simulations in CST Microwave Studio, demonstrate that Evo-HMAA consistently outperforms state-of-the-art standalone and hybrid optimization algorithms. Across array sizes ranging from 2×2 to 20×20 elements, Evo-HMAA achieves up to 118% improvement in directivity, 186% enhancement in realized gain, sidelobe suppression below −59 dB, and impedance matching better than −19 dB. These results confirm Evo-HMAA as a scalable, robust, and electromagnetically realizable optimization framework for next-generation HMIMO antenna systems.

Uche Agwu, Iyemeh Uchendu,"Physics-Aware Hybrid Evolutionary Optimization Framework for Super-Directive Hmimo Antenna Arrays" International Journal of Scientific Research and Engineering Development, V9(1): Page(222-229) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.