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Multi-objective Wolf Pack Algorithm Based on Random Scouting and Hierarchical Learning

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Genetic and Evolutionary Computing (ICGEC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1145))

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Abstract

Since the wolf pack algorithm has the advantages of fast convergence and good robustness in solving single-objective problems, we propose a multi-objective wolf pack algorithm based on random scouting and hierarchical learning (MOWPA-RH) for solving multi-objective optimization problems by combining the excellent hunting habits of wolves. Firstly, a random scouting strategy is used to help the population spread its search range and enhance the global search ability of the people. Secondly, the population is stratified by non-dominated sorting. The individuals in the first layer carry out the Levy-Flight strategy to enhance the ability of the algorithm to jump out of the local optimum, and the individuals in each layer except the first layer are guided by the dominant individuals in the former layer, which is conducive to searching for higher-quality solutions. Finally, the current population is merged with the previous generation, and then the population update is accomplished through the screening mechanism so that the algorithm has good convergence and distribution. Comparing MOWPA-RH with five multi-objective optimization algorithms on 12 different benchmarking problems, the experimental results validate the effectiveness of MOWPA-RH.

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Authors and Affiliations

  1. Nanchang Institute of Technology, Nanchang, 330099, China

    Wenfei Dong, Runxiu Wu, Feng Lv & Jia Zhao

Authors
  1. Wenfei Dong
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  2. Runxiu Wu
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  3. Feng Lv
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  4. Jia Zhao
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Corresponding author

Correspondence to Runxiu Wu .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and Informatic Devices, Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

  2. Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

    Chin-Shiuh Shieh

  3. National University of Science and Technology, Kaohsiung, Taiwan

    Mong-Fong Horng

  4. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dong, W., Wu, R., Lv, F., Zhao, J. (2024). Multi-objective Wolf Pack Algorithm Based on Random Scouting and Hierarchical Learning. In: Lin, J.CW., Shieh, CS., Horng, MF., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2023. Lecture Notes in Electrical Engineering, vol 1145. Springer, Singapore. https://doi.org/10.1007/978-981-97-0068-4_49

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