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An overview of population-based algorithms for multi-objective optimisation

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  • Ioannis Giagkiozis
  • Robin C. Purshouse
  • Peter J. Fleming

Abstract

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided.

Suggested Citation

  • Ioannis Giagkiozis & Robin C. Purshouse & Peter J. Fleming, 2015. "An overview of population-based algorithms for multi-objective optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(9), pages 1572-1599, July.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:9:p:1572-1599
    DOI: 10.1080/00207721.2013.823526
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    Cited by:

    1. Lixia Deng & Huanyu Chen & Xiaoyiqun Zhang & Haiying Liu, 2023. "Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization," Mathematics, MDPI, vol. 11(9), pages 1-13, April.
    2. Yizhang Xia & Jianzun Huang & Xijun Li & Yuan Liu & Jinhua Zheng & Juan Zou, 2023. "A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition," Mathematics, MDPI, vol. 11(2), pages 1-27, January.
    3. Lu Chen & Kaisa Miettinen & Bin Xin & Vesa Ojalehto, 2023. "Comparing reference point based interactive multiobjective optimization methods without a human decision maker," Journal of Global Optimization, Springer, vol. 85(3), pages 757-788, March.
    4. Zou, Juan & Yang, Xu & Liu, Zhongbing & Liu, Jiangyang & Zhang, Ling & Zheng, Jinhua, 2021. "Multiobjective bilevel optimization algorithm based on preference selection to solve energy hub system planning problems," Energy, Elsevier, vol. 232(C).

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