.png){kind=icon}
Return
An adaptive switching-based evolutionary algorithm for many-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2022.108915.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Pareto-based evolutionary algorithms are challenging in dealing with many-objective problems encountering many incomparable nondominated solutions. To reduce selection pressure and improve diversity, this paper proposes an adaptive switching strategy-based evolutionary algorithm for manyobjective optimization. This strategy contains two deletion criteria, which are switched adaptively between generations, aiming to delete poor solutions one by one in environmental selection. The first criterion is devised to delete the solution with poor convergence among the two most similar solutions. The second criterion is developed to delete the worse solution according to a designed indicator that takes into account both convergence and diversity. Finally, comparisons with five state-of-theart many-objective evolutionary algorithms on some widely used benchmark problems and the water resource planning problem are given to illustrate the effectiveness and advantages of the proposed algorithm. (C) 2022 Elsevier B.V. All rights reserved.
Keywords:
Convergence
Diversity
Adaptive switching
Evolutionary algorithm
Many-objective optimization
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach
PREAI
ShareSave
ShareSaveShareSave
View more