According to the distribution characteristics of the Pareto set (PS) of multi-objective optimization problems (MOPs)

a cooperative coevolutionary model with new problem decomposition method was designed.By introducing the proposed coevolutionary model into artificial immune system

a cooperative immune coevolutionary algorithm for multi-objective optimization (CICAMO) was proposed.In CICAMO

the Tchebycheff decomposition method is employed to divide sub-populations at first

and then linear probabilistic models are built for each sub-population to piecewise approximate the distribution of the whole PS.In antibody reproducing step

two types of approaches based on clonal selection and model sampling are employed.Experimental results indicate that CICAMO can achieve a good performance in terms of both solution quality and convergence rate

especially when solving MOPs with non-linear relationship between decision variables.