Pareto Dominance-Based Algorithms With Ranking Methods for Many-Objective Optimization

In Pareto dominance-based multi-objective evolutionary algorithms (PDMOEAs), Pareto dominance fails to provide the essential selection pressure required to drive the search toward convergence in many-objective optimization problems (MaOPs). Recently, the idea of using secondary criterion, such as kn...

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Bibliographic Details
Main Authors: Vikas Palakonda, Rammohan Mallipeddi
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/7950899/

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