Learning to Simultaneously Converge and Diversify Better: UIP Operator
摘要
It has been highlighted earlier that all evolutionary multi- and many-objective optimization algorithms (EMâOAs), including the reference vector (RV)-based EMâOAs or RV-EMâOAs, pursue the dual goals of convergence-to and diversity-across the true Pareto front ( \(P\!F\) ). In previous chapters, IP2 and IP3 operators have been discussed with a focus solely on convergence enhancement and diversity enhancement, respectively.