Constrained Multiobjective Optimization Based on Dynamic Priority and Cooperative Offspring Generation
针对约束多目标优化问题中现有算法处理约束优先级固定且忽视约束间合作导致效率低下的问题，提出一种基于动态优先级和协同子代生成的进化算法，通过动态估计约束不一致程度来调整优先级，并设计约束合作策略提升解质量，在六个测试集上优于六种先进算法。
As the number and complexity of constraints in constrained multi-objective optimization problems (CMOPs) increase, the performance of existing constrained multi-objective evolutionary algorithms (CMOEAs) declines significantly. A novel idea is to sequentially address each constraint based on priority, effectively reducing the complexity of CMOPs. However, in these algorithms, the constraint-handling priority is determined statically in the initial stage. This may lead to inappropriate determination of constraint-handling priority since accurately estimating the constraint landscape in the initial stage is quite challenging. Moreover, these algorithms tackle constraints separately, neglecting the potential for inter-constraint cooperation and thus compromising their efficiency in constraint handling. Thus, we propose a constrained multi-objective evolutionary algorithm based on dynamic priority and cooperative offspring generation called DPCMOEA. Firstly, the constraint-handling priority is determined dynamically by the estimated inconsistency degree (EID) between the Pareto fronts of the candidate constraints and the current population. Secondly, computational resources are automatically allocated to each constraint according to EID based constraint relationship analysis. Finally, a new offspring generation strategy based on constraint cooperation is designed to enhance the quality of new solutions. Experimental results on six CMOP test suites demonstrate that DPCMOEA outperforms six state-of-the-art algorithms.