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Multi-objective Brainstorming Optimization Algorithm Based on Adaptive Mutation Strategy

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Advances in Swarm Intelligence (ICSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12689))

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Abstract

Multi-objective Problems (MOP) is a classic combinatorial optimization problem. A brainstorming optimization algorithm based on multiple adaptive mutation methods in multiple regions of the population (DE_MOBSO) is proposed in this paper to solve the MOP. Firstly, the algorithm uses differential mutation to evolve the population, which can improve the diversity of population. Secondly, an adaptive mutation learning factor is introduced on the mutations to enhance the search efficiency of the algorithm considering the characteristics of the MOP. The effectiveness and practicability of the algorithm are verified by a set of simulation example. The results show that the proposed algorithm has better performance in solving large-scale MOP.

Shaanxi Key R&D Program “Research and Application of Intelligent Service Platform for Complex Heavy Equipment Based on Industrial Internet”, project number: 2020ZDLGR07-06; National Key R&D Program of the Ministry of Science and Technology: “R&D of a Network Collaborative Manufacturing Platform for Customized Manufacturing of Complex Heavy Equipment” 2018YFB1703000.

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Author information

Authors and Affiliations

  1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Yali Wu, Yulong Wang & Xiaoxiao Quan

  2. Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an, 710048, China

    Yulong Wang & Xiaoxiao Quan

Authors
  1. Yali Wu
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  2. Yulong Wang
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  3. Xiaoxiao Quan
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Corresponding author

Correspondence to Yali Wu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

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Wu, Y., Wang, Y., Quan, X. (2021). Multi-objective Brainstorming Optimization Algorithm Based on Adaptive Mutation Strategy. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_42

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  • DOI: https://doi.org/10.1007/978-3-030-78743-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78742-4

  • Online ISBN: 978-3-030-78743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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