The optimal placement and sizing of distributed generation (DG) units in power distribution networks play a crucial role in minimizing power losses, improving voltage stability, and ensuring economic efficiency. However, existing optimization methods, including particle swarm optimization (PSO) and its variants, struggle with premature convergence, fixed parameter tuning, and suboptimal economic trade-offs, particularly when reactive power tariffs are considered. To address these limitations, this study presents a fuzzy-based particle swarm optimization (FPSO) approach, where both inertia weight and acceleration coefficients are adaptively tuned using fuzzy logic. A comparative analysis is conducted between standard PSO, FPSO with inertia weight adaptation, and FPSO with full fuzzy-based tuning to determine the most effective approach for DG allocation in the IEEE-33 bus system. The proposed FAPSO method achieves significant reductions in annual economic losses (AEL), power losses, and voltage deviations, demonstrating superior convergence and cost-effectiveness under reactive power tariff constraints. Simulation results confirm that FPSO with full parameter adaptation outperforms conventional PSO and single-parameter FPSO variants, establishing it as a robust and practical solution for optimizing DG placement in modern smart grids.