关键词: 约束多目标优化问题, 两阶段搜索, 资源分配, 非支配排序, 收敛性, 多样性

Abstract:

The difficulty of solving constrained multi-objective optimization problems lies in balancing objective optimization and constraint satisfaction， while balancing the convergence and diversity of solution sets. To solve complex constrained multi-objective optimization problems with large infeasible regions and small feasible regions， a constrained multi-objective evolutionary algorithm based on Two-Stage search and Dynamic Resource Allocation （TSDRA） was proposed. In the first stage， infeasible regions were crossed by ignoring constraints； in the second stage， two kinds of computing resources were allocated dynamically to coordinate local exploitation and global exploration， while balancing the convergence and diversity of the algorithm. The simulation results on LIRCMOP and MW series test problems show that compared with four representative algorithms of Constrained Multi-objective Evolutionary Algorithm with Multiple Stages （CMOEA-MS）， Two-phase （ToP）， Push and Pull Search （PPS） and Multi Stage Constrained Multi-Objective evolutionary algorithm （MSCMO）， the proposed algorithm obtains better results in both Inverted Generational Distance （IGD） and HyperVolume （HV）. TSDRA obtains 10 best IGD values and 9 best HV values on LIRCMOP series test problems， and 9 best IGD values and 10 best HV values on MW series test problems， indicating that the proposed algorithm can effectively solve problems with large infeasible regions and small feasible regions.

Key words: Constrained Multi-objective Optimization Problem (CMOP), two-stage search, resource allocation, non-dominated sorting, convergence, diversity