Abstract:
Since convergence and diversity are taken into consideration cooperatively in the whole iteration process, the traditional multi-objective optimization algorithms will generate a large number of dominated solutions in the early stage of search, which will inevitably result in the waste of computing resources, even the non-convergence of solving procedure. Aiming at the above shortcoming, this paper proposes a stepwise multi-objective optimization algorithm based on statistical information feedback (SFI-SMOEA), which is composed of three steps, i.e., the optimal value exploration for each objective, rough search for Pareto front, local optimization stage with group division. According to the feature of each step, the corresponding task and strategy is assigned to promote convergence and diversity. In the 2nd and 3rd steps, the solutions are divided into different groups according to the objective function, the information of different regional solutions is statistically analyzed, and then, the statistical feedback information (SFI) of different regional solutions is utilized to guide the parent-selection process. Thus, the distribution and convergence of the solutions can be controlled more precisely. Finally, the proposed algorithm is tested on DTLZ and WFG series issues, and compared with other multi-objective optimization algorithms, from which it is illustrated that the proposed algorithm has the advantages of solving complex and difficult convergence problems.