Many-objective optimization using evolutionary algorithms: A survey
Abstract Multi-objective Evolutionary Algorithms (MOEAs) have proven their effectiveness
and efficiency in solving complex problems with two or three objectives. However, recent …
and efficiency in solving complex problems with two or three objectives. However, recent …
Approximating complex Pareto fronts with predefined normal-boundary intersection directions
Decomposition-based evolutionary algorithms using predefined reference points have
shown good performance in many-objective optimization. Unfortunately, almost all …
shown good performance in many-objective optimization. Unfortunately, almost all …
On the importance of isolated infeasible solutions in the many-objective constrained NSGA-III
Recently, decomposition has gained a wide interest in solving multi-objective optimization
problems involving more than three objectives also known as Many-objective Optimization …
problems involving more than three objectives also known as Many-objective Optimization …
On the importance of isolated solutions in constrained decomposition-based many-objective optimization
During the few past years, decomposition has shown a high performance in solving Multi-
objective Optimization Problems (MOPs) involving more than three objectives, called as …
objective Optimization Problems (MOPs) involving more than three objectives, called as …
Multi-objective evolutionary algorithm for image segmentation
W Abeysinghe, M Wong, CC Hung… - 2019 …, 2019 - ieeexplore.ieee.org
Clustering is an unsupervised learning technique commonly used for image segmentation.
As the outcome of most clustering algorithms is heavily dependent on the initial cluster …
As the outcome of most clustering algorithms is heavily dependent on the initial cluster …