Abstract：To improve the convergence of multi-objective evolutionary algorithm, a measure based on distance is proposed. A density estimation metric-tree crowding distance is defined. The individual distance and the tree crowing distance are used as the selection criteria when the non-dominated front is considered. When the size of non-dominated solution set exceeds that of the population, a method based on neighboring distance is employed to truncate population. By examining of five performance metrics on six test problems, the proposed algorithm is demonstrated to be more competitive in uniformity and spread and performs better in converging to the pareto front, compared to NSGA-II and SPEA2.
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