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Multi-objective Bird Swarm Algorithm

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Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

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Abstract

Most real-world optimization problems involve multiple objectives and parameters. In this paper, bird swarm algorithm (BSA) is modified with non-dominated sorting approach and parallel coordinates. A developed algorithm, known as multi-objective BSA (MOBSA) is proposed. When the external archive for non-dominated solutions is full to overflowing, the solution with greatest density would be rejected. The approaches were tested and compared on benchmark problems. Based on these results, the MOBSA has access to better convergence and spread of Pareto front.

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Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dongmei Wu & Hao Gao

Authors
  1. Dongmei Wu
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  2. Hao Gao
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Correspondence to Dongmei Wu .

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Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Wu, D., Gao, H. (2020). Multi-objective Bird Swarm Algorithm. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_12

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