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

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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 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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Acknowledgements

The first author Dr. Gaurav Dhiman would like to thanks to “Shri Maa Kali Devi” for her divine blessing on him.

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Authors and Affiliations

  1. Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, 147001, India

    Gaurav Dhiman & Harsimran Kaur

  2. Department of Computer Engineering, Smt. S. R. Patel Engineering College, Unjha, Gujarat, 384170, India

    Mukesh Soni

  3. Department of Computer Science, Edge Hill University, Ormskirk, UK

    Hari Mohan Pandey

  4. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

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  1. Gaurav Dhiman
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  5. Harsimran Kaur
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Correspondence to Gaurav Dhiman.

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Dhiman, G., Soni, M., Pandey, H.M. et al. A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization. Engineering with Computers 37, 3017–3035 (2021). https://doi.org/10.1007/s00366-020-00986-0

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