To further improve the diversity and convergence rate of the existed multi-objective evolutionary algorithms, a
co-evolutionary multi-objective optimization algorithm with balanced diversity and convergence(CMOA-BDC) is proposed
specific to the dependency-free multi-objective optimization problems through integrating the cooperative co-evolutionary
model. Firstly, CMOA-BDC sets an elitism set, employs the simple dominant relationship to search the first non-dominant
layer in the evolutionary population and the elitism set, and adopts crowding distance to keep the diversity of the first nondominant
layer. Then cluster analysis is used to divide the first non-dominant layer into multiple class, and the probability
model is established. Finally, a co-evolutionary method is realized by using simulated annealing to integrate the estimation of
distribution and genetic evolution. In comparison with the classical MOEAS, the experimental results show that the algorithm
has better outcomes in both convergence and diversity.