关键词: 精英保留策略, 空间收缩技术, PPS, 收敛性, 多样性

Abstract: Abstract: The reasonable exploration of the infeasible region not only hold population quickly converge to the optimal solution in the feasible region, but also reduced the impact of unpromising infeasible region on the performance of the algorithm when the constrained multi-objective evolutionary algorithm solved the optimization problems with large infeasible region. Based on this, a Constrained Multi-Objective Evolutionary Algorithm based on Space Shrinking Technique (CMOEA-SST) was proposed. On the basis of Push and Pull Search for Solving Constrained Multi-Objective Optimization Problems (PPS) algorithm, an adaptive elite retention strategy was proposed to improve the initial population in the Pull phase of the PPS algorithm, which can increase the diversity and feasibility of the initial population in the Pull phase. The space shrinking technology was used to gradually reduce the search space during the evolution process, which reduced the impact of unpromising infeasible regions. The algorithm can improve the accuracy of the algorithm while balancing the convergence and diversity. In order to verify the performance of CMOEA-SST, it is compared with four representative algorithms(C-MOEA/D、ToP、C-TAEA、PPS) on the test problems of LIRCMOP series. Experimental results show that CMOEA-SST has better convergence and diversity when dealing with optimization problems with larger infeasible regions.

Key words: elite retention strategy, space shrinkage technique, PPS, convergence, diversity