There has emerged a surge of research activity on multiobjective optimization using evolutionary computation in recent years and a number of well performing algorithms have been published. The quick and highly efficient multiobjective evolutionary algorithm based on dominating tree has been criticized mainly for its restricted elite archive and absence of density estimation. This paper improves the algorithm in these two aspects. The nearest distance between the node and other nodes in the same sibling chain is used as its density estimation; the population growing and declining strategies are proposed to avoid the retreating and shrinking phenomenon caused by the restricted elite archive. The simulation results show that the improved algorithm is able to maintain a better spread of solutions and converge better in the obtained nondominated front compared with NSGA-II, SPEA2 and the original algorithm for most test functions.