A Novel Hybrid Evolutionary Algorithm based on Hypervolume Indicator and Reference Vector Adaptation Strategies for Many-Objective Optimization

HARI MOHAN PANDEY, Gaurav Dhiman, Mukesh Soni, Adam Slowik, Harsimran Kaur

Research output: Contribution to journalArticle

Abstract

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 (IGD)and Hypervolume (HV) 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.
Original languageEnglish
Article numberEWCO-D-19-00247R1
JournalEngineering with Computers
Early online date25 Feb 2020
Publication statusE-pub ahead of print - 25 Feb 2020

Keywords

  • Many-objective optimization
  • Hypervolume estimation algorithm;
  • Reference vector guided evolutionary algorithm
  • Constrained optimization
  • Pareto optimality

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