In order to solve the problem that the population diversity preservation strategy only based on crowding distance of Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) cannot reflect the real crowding degree of individuals, an improved NSGA-Ⅱ algorithm based on the adaptive hybrid non-dominated individual sorting strategy (NSGA-Ⅱ ^h) was proposed. First, a novel loop-clustering individual sorting strategy was designed. Second, according to the Pareto layer-sorting information the NSGA-Ⅱ ^h algorithm adaptively chose one from the two individual sorting strategies based on classical crowding distance and loop-clustering. Finally, the diversity maintain mechanism could be improved especially during the late period of evolutionary optimization. The NSGA-Ⅱ ^h algorithm was compared with three classical algorithms including NSGA-Ⅱ, Multi-Objective Particle Swarm Optimization (MOPSO) and GDE3. The experiments on five multi-objective benchmark functions show that the NSGA-Ⅱ ^h algorithm can acquire 80% of optimal Inverted Generational Distance (IGD) values, and the corresponding two-tailed t-test results at a 0.05 level of significance are remarkable. The proposed algorithm can not only improve convergence of the original algorithm, but also enhance the distribution of Pareto optimal set.