Paper Info
Title
KnRVEA: A hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization
Abstract
In this paper, a many-objective evolutionary algorithm, named as a hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies (KnRVEA) is proposed. Knee point strategy is used to improve the convergence of solution vectors. In the proposed algorithm, a novel knee adaptation strategy is introduced to adjust the distribution of knee points. KnRVEA is compared with five well-known evolutionary algorithms over thirteen benchmark test functions. The results reveal that the proposed algorithm provides better results than the others in terms of Inverted Generational Distance and Hypervolume. The computational complexity of the proposed algorithm is also analyzed. The statistical testing is performed to show the statistical significance of proposed algorithm. The proposed algorithm is also applied on three real-life constrained many-objective optimization problems to demonstrate its efficiency. The experimental results show that the proposed algorithm is able to solve many-objective real-life problems.
Year
DOI
Venue
2019
10.1007/s10489-018-1365-1
Applied Intelligence
Keywords
Field
DocType
Evolutionary multi-objective optimization, Many-objective optimization, Convergence, Diversity
Convergence (routing),Inverted generational distance,Evolutionary algorithm,Computer science,Reference vector,Algorithm,Artificial intelligence,Optimization problem,Machine learning,Statistical hypothesis testing,Computational complexity theory
Journal
Volume
Issue
ISSN
49
7
0924-669X
Citations 
PageRank 
References 
4
0.38
50
Authors
2
Name
Order
Citations
PageRank
Gaurav Dhiman163632.82
Vijay Kumar222921.59