A Unified Innovized Progress Operator for Performance Enhancement in Evolutionary Multi- and Many-Objective Optimization

This article 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 intergenerational solutions along the different reference vectors (RVs). This article first demonstrates that ML intervention can also help enhance the diversity capability of RV-EMâOAs through mapping of intragenerational 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.