A self-adaptive multi-objective optimization algorithm based on the Pareto´s non-dominated sets

In order to improve the optimization efficiency in the multi-objective fault optimization, the self-adaptive multi-objective optimization algorithm based on the Pareto´s non-dominated sets by binary tree (SMOS) are proposed in the paper. Firstly, the Self-adaptive adjustment of inertia weight is put forward to adjust the fitness function based on niche sharing mechanism. Secondly, the Pareto non-dominated sets are constructed by the binary tree to improve the optimization efficiency. Then, the SMOS algorithm is present reduce the optimized time complexity of constructed Pareto non-dominated sets when the optimized object number are larger. Meanwhile that the constructed non-dominated sets belongs the Pareto sets is proved. Finally the simulation results show when the numbers of non-dominated population are more than 5, the non-dominated efficiency can improve approximately 50%.