Comparison of GA, SGOA, and PSO for Linear Antenna Array Optimization - Budida, Jyothi; Srinivasan, Kamala (Journal of Neonatal Surgery, 2025)
The performance of a linear antenna array is crucial in applications such as wireless communication, radar, and satellite systems. Achieving optimal radiation characteristics, including minimized sidelobe levels, enhanced directivity, and efficient beam steering, requires advanced optimization techniques. This research paper presents a comparative analysis of three prominent optimization algorithms—Genetic Algorithm (GA), Social Group Optimization Algorithm (SGOA), and Particle Swarm Optimization (PSO)—for optimizing linear antenna arrays. GA, inspired by the principles of natural selection and evolution, uses genetic operators such as selection, crossover, and mutation to explore the search space and find an optimal solution. SGOA is a social learning-based algorithm that models individual learning, social learning, and group influence to enhance convergence efficiency and solution accuracy. PSO, based on swarm intelligence, optimizes the antenna array by adjusting element excitations and positions through the collective movement of particles in a multidimensional search space. The study evaluates these algorithms based on key performance metrics such as sidelobe suppression, directivity enhancement, convergence speed, computational complexity, and radiation efficiency. Through extensive simulations, it is observed that GA effectively reduces sidelobe levels but suffers from slow convergence due to the stochastic nature of genetic operations. SGOA offers superior adaptability and faster convergence, making it suitable for real-time applications. PSO, known for its computational efficiency, demonstrates quick convergence but can struggle with local optima in highly complex optimization scenarios. The findings of this study provide valuable insights into selecting the most appropriate optimization technique based on specific antenna array design requirements. Future research can focus on hybrid approaches and machine learning-based optimizations to further improve antenna performance.