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Designing the high-performance, high-subsonic airfoils suitable for different Reynolds number flow is the major challenge for high-altitude long-endurance drones and ultra-high altitude solar aircraft. To overcome the difficulties, this paper proposed a set of aerodynamic optimization design methods that combine the improved category shape function transformation method, parallel computational fluid dynamics, and an improved whale optimization algorithm. This approach achieves high modeling accuracy, computational efficiency, and strong optimization ability. Verification using single-peak, multi-peak, and fixed-dimensional test functions demonstrates that the improved whale optimization algorithm has significantly enhanced optimization capabilities compared with the conventional whale optimization, particle swarm optimization, and gravity search algorithms. Numerical simulations are used to analyze the internal flow mechanisms before and after airfoil optimization under different Reynolds numbers. The results show that, compared with the original airfoil, the optimized airfoil has front loading characteristics that effectively reduce the adverse pressure gradient of the rear part of suction surface, promoting the transition in this region. The reduced adverse pressure gradient also inhibits the development and growth of laminar separation bubbles and slows down the growth of the boundary layer displacement thickness, thereby reducing wake mixing loss and improving the aerodynamic performance. [ABSTRACT FROM AUTHOR]