A multiobjective optimization algorithm inspired from cloud model and using favour ranking is introduced. The innovation of the algorithm lies in the estimation of good solution regions and new solution production according to the cloud model theory. The algorithm used information obtained during optimization to build the cloud model for good solution regions

and estimated three digital characteristics of the cloud model by backward cloud generators. Afterwards

forward cloud generators were used to generate current offsprings population according to three digital characteristics. The population with the current population and current offsprings population was sorted using favour ranking

and the best individuals were selected to form the next population. Regarding a set of benchmark functions

the proposed algorithm was tested and compared with some other algorithms. The experimental results show that the algorithm is effective in the benchmark functions.