Evolutionary algorithms (EAs) are widely used optimization techniques driven by fitness-based selection. Conventional fitness functions are usually directly defined by the original objective function or its simple transformation. However, these conventional fitness assignment strategies often lead to reduced population diversity and premature convergence. Inspired by the Mach number in gas dynamics, this paper proposes a Mach-number-based fitness assignment strategy (Ma-fitness). The wave solution of EAs describes the propagation characteristics of the population particle density wave. By modeling the population as a dynamic system, the Ma-fitness strategy evaluates individuals based on the ratio between their motion velocity and the propagation speed of the population particle density wave. This strategy measures the motion velocity of an individual by the difference between its values of objective function at two consecutive generations. Meanwhile, the propagation speed of the particle density wave is represented as the convergence speed of the population. This dynamic measure captures both solution quality and search potential. Integrated into genetic algorithms (GAs) and particle swarm optimization (PSO), this strategy is validated on the CEC-2013 benchmark suite. Experimental results show that Ma-fitness improves the convergence, stability, and generalization for EAs and PSO, demonstrating its effectiveness and applicability.