Parabolic trough collectors (PTCs) are essential components in concentrated solar power systems, offering high thermal conversion efficiencies by concentrating solar radiation onto a receiver tube. However, optimizing PTC design remains a complex, multi-objective challenge involving conflicting thermal and exergetic performance criteria. This study explores the application of five nature-inspired metaheuristic algorithms—Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), Sunflower Optimization Algorithm (SFO), Imperialist Competitive Algorithm (ICA), and Grasshopper Optimization Algorithm (GOA)—for the optimal design of PTC receiver parameters, including inlet temperature, trough length, and receiver tube dimensions. A detailed mathematical model encompassing thermal and exergetic efficiency was developed, and both single- and multi-objective optimization problems were solved using these algorithms. Comparative analyses based on convergence rate, solution stability, and Pareto front diversity revealed that while all algorithms achieved the same maximum efficiency metrics (ηth = 31.63%, ηex = 8.0011%), their search dynamics and solution characteristics varied significantly. HHO and WOA showed rapid convergence, SFO and GOA offered diverse exploration patterns, and ICA and MOGOA achieved well-balanced trade-offs. The results underscore the efficacy of bio-inspired metaheuristics in addressing the complex trade-offs inherent in PTC optimization and provide valuable insights into algorithm suitability for solar thermal system design.