The Density Peak Clustering （DPC） algorithm cannot accurately select the cluster centers for the datasets with various density and complex shape. The Clustering by Local Gravitation （LGC） algorithm has many parameters which need manual adjustment. To address these issues， a new Clustering algorithm based on Local Gravity and Distance （LGDC） was proposed. Firstly， the local gravity model was used to calculate the ConcEntration （CE） of data points， and the distance between each point and the point with higher CE value was determined according to CE. Then， the data points with high CE and high distance were selected as cluster centers. Finally， the remaining data points were allocated based on the idea that the CE of internal points of the cluster was much higher than that of the boundary points. At the same time， the balanced k nearest neighbor was used to adjust the parameters automatically. Experimental results show that， LGDC achieves better clustering effect on four synthetic datasets. Compared with algorithms such as DPC and LGC， LGDC has the index of Adjustable Rand Index （ARI） improved by 0.144 7 on average on the real datasets such as Wine， SCADI and Soybean.

The reasonable exploration of the infeasible region in constrained multi-objective evolutionary algorithms for solving optimization problems with large infeasible domains not only helps the population to converge quickly to the optimal solution in the feasible region， but also reduces the impact of unpromising infeasible region on the performance of the algorithm. Based on this， a Constrained Multi-Objective Evolutionary Algorithm based on Space Shrinking Technique （CMOEA-SST） was proposed. Firstly， an adaptive elite retention strategy was proposed to improve the initial population in the Pull phase of Push and Pull Search for solving constrained multi-objective optimization problems （PPS）， so as to increase the diversity and feasibility of the initial population in the Pull phase. Then， the space shrinking technique was used to gradually reduce the search space during the evolution process， which reduced the impact of unpromising infeasible regions on the algorithm performance. Therefore， the algorithm was able to improve the convergence accuracy while taking account of both convergence and diversity. In order to verify the performance of the proposed algorithm， it was simulated and compared with four representative algorithms including C-MOEA/D （adaptive Constraint handling approach embedded MOEA/D）， ToP （handling constrained multi-objective optimization problems with constraints in both the decision and objective spaces）， C-TAEA （Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization） and PPS on the test problems of LIRCMOP series. Experimental results show that CMOEA-SST has better convergence and diversity when dealing with constrained optimization problems with large infeasible regions.