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Multi-objective Baby Search Algorithm

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Advances in Swarm Intelligence (ICSI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13968))

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Abstract

Multi-objective optimization problems are commonplace in real-world applications, and evolutionary algorithms are successful in solving them. Baby search algorithm is a novel evolutionary algorithm proposed recently, which has excellent ability on exploration and exploitation. However, it is designed to cater to single-objective optimization problems, but in this paper, we expand and modify it for multi-objective optimization. We introduce the boundary selection strategy to choose individuals from the Pareto archive for generating new solutions. To determine the best position of each individual we combine Pareto domination relation with random selection. Additionally, we propose an adapted Levy flight method to find promising solutions. Eleven standard multi-objective testing instances, five prevailing indicators and five state-of-art algorithms are applied to evaluate our algorithm. Experiments results demonstrate that our algorithm performs well on IGD, HV, Spread and GD measures.

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Author information

Authors and Affiliations

  1. Academy of Military Science, Beijing, 100091, China

    Yi Liu, Wei Qin, Xiang Li, Kun Liu, Qiang Wang & Qibin Zheng

  2. National Innovation Institute of Defense Technology, Beijing, 100071, China

    Gengsong Li

Authors
  1. Yi Liu
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  2. Gengsong Li
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  3. Wei Qin
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  4. Xiang Li
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  5. Kun Liu
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  6. Qiang Wang
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  7. Qibin Zheng
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Corresponding author

Correspondence to Qibin Zheng .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

  3. Harbin Institute of Technology, Shenzhen, China

    Wenjian Luo

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Liu, Y. et al. (2023). Multi-objective Baby Search Algorithm. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_21

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  • DOI: https://doi.org/10.1007/978-3-031-36622-2_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-36622-2

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