A Parameterless Penalty Rule-Based Fitness Estimation for Decomposition-Based Many-Objective Optimization Evolutionary Algorithm

A Parameterless Penalty Rule-Based Fitness Estimation for Decomposition-Based Many-Objective Optimization Evolutionary Algorithm

Many-objective optimization problems (MaOPs) present a huge challenge to the traditional Pareto-based multi-objective algorithms because the increase of the objectives results in the low-efficiency of the Pareto dominance in distinguishing the relationships between the solutions during the environme...

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Bibliographic Details
Main Authors: Junhua Liu, Yuping Wang, Shiwei Wei, Xiangjuan Wu, Wuning Tong
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Balanceable fitness estimation
dominance method
convergence
diversity
many-objective optimization
selection pressure
Online Access:https://ieeexplore.ieee.org/document/8730321/

Internet

https://ieeexplore.ieee.org/document/8730321/

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