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Multi-objective fitness-dependent optimizer algorithm

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Abstract

This paper proposes the multi-objective variant of the recently-introduced fitness dependent optimizer (FDO). The algorithm is called a multi-objective fitness dependent optimizer (MOFDO) and is equipped with all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the performance-proof purpose: classical ZDT test functions, which is a widespread test suite that takes its name from its authors Zitzler, Deb, and Thiele, and on IEEE Congress of Evolutionary Computation benchmark (CEC-2019) multi-modal multi-objective functions. MOFDO results are compared to the latest variant of multi-objective particle swarm optimization, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. The comparative study shows the superiority of MOFDO in most cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g., welded beam design problems). It is observed that the proposed algorithm successfully provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

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Author information

Authors and Affiliations

  1. Information Technology, College of Commerce, University of Sulaimani, Sulaymaniyah, Iraq

    Jaza M. Abdullah & Bestan B. Maaroof

  2. Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq

    Tarik A. Rashid

  3. Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, Australia

    Seyedali Mirjalili

  4. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  5. School of Information Technology, Fanshawe College, London, ON, Canada

    Bestan B. Maaroof

Authors
  1. Jaza M. Abdullah
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  2. Tarik A. Rashid
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  4. Seyedali Mirjalili
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Correspondence to Tarik A. Rashid.

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Appendix

Appendix

See Tables 7 and 8.

Table 7 ZDT benchmark mathematical definition [35]
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Table 8 CEC 2019 multi-modal multi-objective benchmark mathematical definition [55]
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Abdullah, J.M., Rashid, T.A., Maaroof, B.B. et al. Multi-objective fitness-dependent optimizer algorithm. Neural Comput & Applic 35, 11969–11987 (2023). https://doi.org/10.1007/s00521-023-08332-3

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