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Non-dominated Sorting Based Fireworks Algorithm for Multi-objective Optimization

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13344))

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Abstract

Multi-objective optimization is one of the most important problem in the mathematical optimization. Some researchers have already proposed several multi-objective fireworks algorithms, of which S-metric based multi-objective fireworks algorithm (S-MOFWA) is the most representative work. S-MOFWA takes the hypervolume as the evaluation criterion of external archive updating, which is easy to implement but ignores the landscape information of the population. In this paper, a novel multi-objective fireworks algorithm named non-dominated sorting based fireworks algorithm (NSFWA) is proposed. The proposed algorithm updates the external archive with the selection operator based on the fast non-dominated sorting approach, which is specially designed for the spark generation characteristic of FWA to improve the diversity. A multi-objective guided mutation operator is also designed to enhance the efficiency of population information utilization and improve the search capability of the algorithm. Experimental results on the benchmarks demonstrate that NSFWA outperforms other multi-objective swarm intelligence algorithms of S-MOFWA, NSGA-II and SPEA2.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 62076010), and partially supported by Science and Technology Innovation 2030 - “New Generation Artificial Intelligence” Major Project (Grant Nos.: 2018AAA0102301 and 2018AAA0100302).

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of Artificial Intelligence, Institute for Artificial Intelligence, Peking University, Beijing, China

    Mingze Li & Ying Tan

Authors
  1. Mingze Li
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  2. Ying Tan
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Corresponding author

Correspondence to Ying Tan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Li, M., Tan, Y. (2022). Non-dominated Sorting Based Fireworks Algorithm for Multi-objective Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13344. Springer, Cham. https://doi.org/10.1007/978-3-031-09677-8_38

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  • DOI: https://doi.org/10.1007/978-3-031-09677-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09676-1

  • Online ISBN: 978-3-031-09677-8

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