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MOSA/D: Multi-operator evolutionary many-objective algorithm with self-adaptation of parameters based on decomposition

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Abstract

In the domain of science and engineering optimization, many-objective problems have posed great challenges during the last few years. In response to this, several algorithms have been proposed that include generic as well as specific approaches for problem-solving. This paper proposes an improved evolutionary many-objective optimization (EMaO) method, MOSA/D, which is a Multi-Operator approach with Self-Adaptation of parameters based on Decomposition. It is a generic method based on the framework of the MOEA/D algorithm and aimed to explore the role of an ensemble of differential evolution operators for optimizing many-objective problems in the decomposition environment. Another contribution of this study is the detection of the start of the exploitation stage of evolution to enable the fine-tuning of solutions. The simulation studies involve comparing our approach with some selected state-of-the-art algorithms on standard test problems having 5-, 8-, 10-, 12- and 15-objective spaces. The experimental results show that the proposed MOSA/D is overall superior to other approaches in terms of attaining the convergence and diversity of solutions.

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Data Availability

All data generated or analyzed during this study are included in this published article as different tables.

Notes

  1. For DTLZ problems

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Funding

This work was supported by the MoST (Ministry of Science & Technology) endowment and NED University research grants.

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  1. Muhammad Ali Ismail

    Present address: NED University of Engineering & Technology, Karachi, Pakistan

Authors and Affiliations

  1. NED University of Engineering & Technology, Karachi, Pakistan

    Syed Zaffar Qasim

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  1. Syed Zaffar Qasim
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Correspondence to Syed Zaffar Qasim.

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As mentioned earlier, code is developed in Java using jMetal framework and is available with the authors.

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Qasim, S.Z., Ismail, M.A. MOSA/D: Multi-operator evolutionary many-objective algorithm with self-adaptation of parameters based on decomposition. Evol. Intel. 16, 849–871 (2023). https://doi.org/10.1007/s12065-021-00698-4

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