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Multi-objective Optimization

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Intelligent Optimization

Abstract

In recent decades, evolutionary multi-objective optimization has attracted a growing interest due to the fact that many real-world applications are multi-objective optimization problems (MOPs). This chapter introduces basic concepts regarding multi-objective optimization, then several popular multi-objective optimization evolutionary algorithms (MOEAs) are described, and performance measures and visualization of Pareto front are also introduced.

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Authors and Affiliations

  1. School of Artificial Intelligence, Anhui University of Science and Technology, Hefei, Anhui, China

    Changhe Li

  2. School of Artificial Intelligence, Anhui University of Sciences & Technology, Hefei, Anhui, China

    Shoufei Han

  3. School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, Hubei, China

    Sanyou Zeng

  4. School of Computer Science and Informatics, De Montfort University, Leicester, UK

    Shengxiang Yang

Authors
  1. Changhe Li
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  2. Shoufei Han
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  3. Sanyou Zeng
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  4. Shengxiang Yang
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Li, C., Han, S., Zeng, S., Yang, S. (2024). Multi-objective Optimization. In: Intelligent Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-97-3286-9_9

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  • DOI: https://doi.org/10.1007/978-981-97-3286-9_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-3285-2

  • Online ISBN: 978-981-97-3286-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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