A Unified Innovized Progress Operator for Performance Enhancement in Evolutionary Multi- and Many-Objective Optimization
提出一种基于机器学习的统一创新进展算子，同时增强参考向量进化多目标和超多目标优化算法的收敛性和多样性，在约92%的测试实例中性能优于或等同于基础算法。
This paper proposes a machine learning (ML) based unified innovized progress (UIP) operator to simultaneously enhance the convergence and diversity capabilities of reference vector based evolutionary multi-and many-objective optimization algorithms, namely, RV-EMâOAs. Recent studies have demonstrated that ML intervention could help enhance convergence of RV-EMâOAs by capturing efficient search directions, through mapping of inter-generational solutions along the different reference vectors (RVs). This paper first demonstrates that ML intervention can also help enhance the diversity capability of RV-EMâOAs through mapping of intra-generational solutions across the RVs. Subsequently, the UIP operator integrates the convergence and diversity enhancement capabilities in a manner that is generic -applicable to different RV-EMâOAs, and practicable -not requiring any extra solution evaluations over the base RV-EMâOAs. Based on 24,056 experimental runs on multi-and many-objective problems, the UIP operator, when integrated with different RV-EMâOAs, has provided statistically better performance in about 36% instances, and better or equivalent in about 92% instances, compared to the respective base RV-EMâOAs.