Abstract: A cloud model-based multi-objective evolutionary algorithm (CMOEA) is proposed based on the multiobjectiveevolutionary algorithm. In CMOEA, a new mutation operator that adaptively adjusts the mutation probabilityis designed to guarantee the good local searching ability. To maintain the diversity of solutions, the niche technology isexploited, where the niche radius is dynamically adjusted according to the X conditions cloud generator. Meanwhile, the dynamiccalculation of crowding distance for individuals and the estimation of the individual congestion intensity by the cloudmodel are conducted at the same time, which is then followed by the eliminating process that removes the excess populationone by one to keep non-inferior solutions for distribution. Finally, the multi-objective 0/1 knapsack problem is employedto test the performance of CMOEA. Experimental results indicate that compared with the currently most effective multiobjectiveevolutionary algorithms (NSGA-II and SPEA2), CMOEA has a better performance in searching and populationdiversity. In addition, fast convergence to the Pareto front is also achieved and the resulting set of Pareto optimal solutionshas superior convergence and distribution.