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Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing

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Advances in Swarm Intelligence (ICSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12690))

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Abstract

Tracking changing Pareto front (PF) in the objective space and Pareto set (PS) in the decision space is an important task in dynamic multi-objective optimization (DMO). Similarly, maintaining population diversity and reusing previous evolutionary information are useful to explore promising regions and to find high-quality solutions quickly in time-varying environments. To this end, a sliding time window based on parallel computing (STW-PC) is introduced in the present study. In the STW-PC, obtained time-sequence solution sets aim to preserve the diversity and facilitate a fast convergence since problems in successive time/environments are usually related. The parallel computing method is also employed to reduce the computational time. The STW-PC is incorporated into a multi-objective evolutionary algorithm and is compared with two competitors on 12 dynamic multi-objective optimization problems. The results show that the STW-PC can both improve the tracking performance of the selected algorithm in different degrees of changes, and significantly reduce the calculation time compared with transfer learning.

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Acknowledgement

This work was partially supported by the Shanghai Science and Technology Innovation Action Plan (18550720100, 19040501600), the National Nature Science Foundation of China (No. 61603244).

Author information

Authors and Affiliations

  1. Logistics Research Center, Shanghai Maritime University, Shanghai, 201306, China

    Qinqin Fan & Yihao Wang

  2. School of Electronic and Computer Engineering, Purdue University, West Lafayette, IN, 47906, USA

    Okan K. Ersoy

  3. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China

    Ning Li

  4. Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

    Zhenzhong Chu

Authors
  1. Qinqin Fan
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  2. Yihao Wang
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  3. Okan K. Ersoy
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  4. Ning Li
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  5. Zhenzhong Chu
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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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Fan, Q., Wang, Y., Ersoy, O.K., Li, N., Chu, Z. (2021). Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-78811-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78810-0

  • Online ISBN: 978-3-030-78811-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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