A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization

A novel hybrid many-objective evolutionary algorithm called Reference Vector Guided Evolutionary Algorithm based on hypervolume indicator (H-RVEA) is proposed in this paper. The reference vectors are used in a number of sub-problems to decompose the optimization problem. An adaptation strategy is us...

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Vydáno v:Engineering with computers Ročník 37; číslo 4; s. 3017 - 3035
Hlavní autoři: Dhiman, Gaurav, Soni, Mukesh, Pandey, Hari Mohan, Slowik, Adam, Kaur, Harsimran
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.10.2021
Springer Nature B.V
Témata:
Adaptation
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Evolutionary algorithms
Genetic algorithms
Math. Applications in Chemistry
Mathematical and Computational Engineering
Multiple objective analysis
Optimization
Original Article
Statistical tests
Systems Theory
Pareto optimality
Hypervolume estimation algorithm
Many-objective optimization
Reference vector guided evolutionary algorithm
Constrained optimization
ISSN:0177-0667, 1435-5663
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Shrnutí:A novel hybrid many-objective evolutionary algorithm called Reference Vector Guided Evolutionary Algorithm based on hypervolume indicator (H-RVEA) is proposed in this paper. The reference vectors are used in a number of sub-problems to decompose the optimization problem. An adaptation strategy is used in the proposed algorithm to adjust the reference vector distribution. The proposed algorithm is compared over well-known benchmark test functions with five state-of-the-art evolutionary algorithms. The results show H-RVEA’s superior performance in terms of the inverted generational distance and hypervolume performance measures than the competitor algorithms. The suggested algorithm’s computational complexity is also analysed. The statistical tests are carried out to demonstrate the statistical significance of the proposed algorithm. In order to demonstrate its efficiency, H-RVEA is also applied to solve two real-life constrained many-objective optimization problems. The experimental results indicate that the proposed algorithm can solve the many-objective real-life problems. Note that the source codes of the proposed technique are available at http://dhimangaurav.com/ .
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-020-00986-0