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Multi-objective Biological Survival Optimizer with Application in Engineering Problems

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2061))

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Abstract

Biological survival optimizer is a recent proposed swarm-based optimization algorithm, which is inspired by the nature behavior of prey and has two significant components, escape phase and adjustment phase. This paper proposes multi-objective Biological survival optimizer (MOBSO) based on non-dominated framework, in which an external set is utilized to store the obtained non-dominated solutions for guiding search. Besides that, elite selection mechanism is also employed to choose promising solutions from parent and offspring agents based on the non-dominated levels. The performance of MOBSO is also evaluated on a suite of benchmark problems with various features and three classical engineering design problems. Simulation comparison results considering different indicators show that MOBSO can generate competitive results compared with other state-of-art optimization techniques.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants 62006103, in part by the Postgraduate research and practice innovation program of Jiangsu province under Grand KYCX22\(\_\)2858, in part by Xuzhou Basic Research Program under Grand KC23025, in part by the Royal Society International Exchanges Scheme IEC\(\backslash \)NSFC\(\backslash \)211404, and in part by China Scholarship Council under Grand 202310090064.

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Authors and Affiliations

  1. Jiangsu Normal University, Xuzhou, 221116, China

    Xueliang Fu & Qingyang Zhang

Authors
  1. Xueliang Fu
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  2. Qingyang Zhang
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Correspondence to Qingyang Zhang .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Central South University, Changsha, China

    Yong Wang

  3. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

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Fu, X., Zhang, Q. (2024). Multi-objective Biological Survival Optimizer with Application in Engineering Problems. In: Pan, L., Wang, Y., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2061. Springer, Singapore. https://doi.org/10.1007/978-981-97-2272-3_15

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  • DOI: https://doi.org/10.1007/978-981-97-2272-3_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2271-6

  • Online ISBN: 978-981-97-2272-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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