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A Memory-Based NSGA-II Algorithm for Dynamic Multi-objective Optimization Problems

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9598))

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Abstract

Dynamic multi-objective optimization problems (DMOPs) have been rapidly attracting the interest of the research community. Although static multi-objective evolutionary algorithms have been adapted for solving the DMOPs in the literature, some of those extensions may have high running time and may be inefficient for the given set of test cases. In this paper, we present a new hybrid strategy by integrating the memory concept with the NSGA-II algorithm, called the MNSGA-II algorithm. The proposed algorithm utilizes an explicit memory to store a number of non-dominated solutions using a new memory updating technique. The stored solutions are reused in later stages to reinitialize part of the population when an environment change occurs. The performance of the MNSGA-II algorithm is validated using three test functions from a framework proposed in a recent study. The results show that performance of the MNSGA-II algorithm is competitive with the other state-of-the-art algorithms in terms of tracking the true Pareto front and maintaining the diversity.

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Acknowledgements

The authors would like to thank Dr. Chi Keong Goh for providing the source code for the dCOEA algorithm. The authors also would like to thank Prof. Shengxiang Yang and Shouyong Jiang for the help regarding their Dynamic Test Problems for Dynamic Multi-objective Optimization.

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Authors and Affiliations

  1. Computer Engineering Department, Marmara University, 34722, Istanbul, Turkey

    Shaaban Sahmoud & Haluk Rahmi Topcuoglu

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  1. Shaaban Sahmoud
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  2. Haluk Rahmi Topcuoglu
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Corresponding author

Correspondence to Haluk Rahmi Topcuoglu .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Sahmoud, S., Topcuoglu, H.R. (2016). A Memory-Based NSGA-II Algorithm for Dynamic Multi-objective Optimization Problems. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9598. Springer, Cham. https://doi.org/10.1007/978-3-319-31153-1_20

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  • DOI: https://doi.org/10.1007/978-3-319-31153-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31152-4

  • Online ISBN: 978-3-319-31153-1

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