Abstract: Due to the sharp increasing of the proportion of Pareto non-dominated candidate solutions for many-objective optimization problems， the commonly used many-objective evolutionary algorithms encounter the selection pressure deterioration problem considering the convergence-first-and-diversity-second selection approach. This paper proposed a many-objective evolutionary algorithm with the diversity-first-and-convergence-second selection strategy-steady-state many-objective evolutionary algorithm based on objective space partition (SS-OSP). Firstly， it divided objective space into a large number of subspaces using a set of weight vectors. Then， one individual in each subspace was selected via adopting aggregation function. In addition， since the penalty parameter of PBI aggregation function remained constant in evolutionary process， an adaptive PBI aggregation function was proposed. Finally， the experimental results show that better convergence and diversity can be obtained using the proposed algorithm.

Key words: many-objective optimization problems, steady-state evolutionary algorithm, objective space partition, adaptive PBI aggregation function, decomposition strategy