Swarm intelligence (SI) techniques have garnered significant attention for optimizing complex, nonlinear systems due to their adaptive, decentralized, and self-organizing nature. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization are two swarm intelligence techniques discussed in this chapter that aim to enhance the performance of quasi-source inverters (QZSI) in grid-connected renewable energy systems. QZSI is well-known for its ability to increase voltage and perform inversion in a single stage, which results in improved power density, reduced switching stress, and excellent reliability. This makes it suitable for integrating wind and solar photovoltaic (PV) systems. However, these hybrid systems’ nonlinear characteristics and dynamic behaviour challenge conventional control methods. This study uses SI-based algorithms to optimize the parameters of PI controllers for QZSI to ensure optimal power flow, fast dynamic response and minimized total harmonic distortion (THD). The PSO and ABC algorithms dynamically fine-tune the control parameters by searching for optimal solutions in a multidimensional space. This results in higher tracking accuracy and system stability under varying grid conditions. The comparative analysis highlights the two algorithms’ strengths: faster PSO convergence and greater ABC flexibility for local optima avoidance. Simulation results demonstrate significant improvements in efficiency, voltage regulation, and transient response, affirming the effectiveness of SI-based techniques in renewable energy integration. This research provides a scalable framework for optimizing QZSI-based wind-PV cogeneration systems, contributing to sustainable and reliable energy networks.