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Multi-objective Emergency Resource Dispatch Based on Coevolutionary Multiswarm Particle Swarm Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

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Abstract

Emergency resource dispatch plays a significant role in the occurrence of emergency events. An efficient schedule solution not only can deliver the required resources in time, but also can reduce the loss in the disaster area. Recently, many scholars are dedicated to dealing with emergency resource dispatch problem (ERDP) by constructing a model with one objective (e.g., the cost to transport resources or the satisfaction degree of people in the disaster area) and solving the model with single objective optimization algorithms. In this paper, we build a multi-objective model that considers both the cost objective and the satisfaction objective, which takes into account multiple retrieval depots and multiple kinds of resources. We propose to solve this multi-objective ERDP optimization model via the recently famous coevolutionary multiswarm particle swarm optimization (CMPSO) algorithm. Based on multiple populations for multiple objectives (MPMO) framework, the CMPSO algorithm uses two populations to optimize the above two objectives respectively, and leads particles to find Pareto optimal solutions by storing information of different populations in a shared archive. We construct ERDP with various scales to validate the feasibility of the applied CMPSO algorithm. Moreover, by setting the satisfaction objective as the constraint, we also compare the results obtained by CMPSO with those obtained by constrained single objective particle swarm optimization (PSO) algorithm. Experimental results show that: 1) the nondominated solutions obtained by CMPSO perform well in both convergence and diversity on two objectives; 2) the results on the cost objective obtained by CMPSO are generally superior to those of PSO under same degree of satisfaction.

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Acknowledgments

This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2102102, in part by the Outstanding Youth Science Foundation under Grant 61822602, in part by the National Natural Science Foundations of China (NSFC) under Grant 61772207 and Grant 61873097, in part by the Key-Area Research and Development of Guangdong Province under Grant 2020B010166002, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, and in part by the Guangdong-Hong Kong Joint Innovation Platform under Grant 2018B050502006.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 51006, China

    Si-Chen Liu & Zhi-Hui Zhan

  2. School of Software Engineering, South China University of Technology, Guangzhou, 51006, China

    Chunhua Chen

  3. Victoria University, Melbourne, VIC, 8001, Australia

    Jun Zhang

Authors
  1. Si-Chen Liu
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  2. Chunhua Chen
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  3. Zhi-Hui Zhan
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  4. Jun Zhang
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Editor information

Editors and Affiliations

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

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

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

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Liu, SC., Chen, C., Zhan, ZH., Zhang, J. (2021). Multi-objective Emergency Resource Dispatch Based on Coevolutionary Multiswarm Particle Swarm Optimization. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_59

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

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