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What is Evolutionary Algorithm for Multi-Objective Optimization about?
This document presents a clustering and dimensionality reduction based evolutionary algorithm designed to tackle large-scale multi-objective problems (MOPs) by categorizing decision variables into diversity and convergence related groups. The algorithm employs principal components analysis (PCA) to reduce dimensionality and improve convergence, demonstrating significant performance enhancements over existing methods in tests involving up to 5000 variables. The proposed approach aims to optimize computationa
- Author
- Abhishek Bhatia
- Language
- EN