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Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems

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Abstract

This paper proposes a novel and an effective multi-objective optimization algorithm named multi-objective sine-cosine algorithm (MO-SCA) which is based on the search technique of sine-cosine algorithm (SCA). MO-SCA employs the elitist non-dominated sorting and crowding distance approach for obtaining different non-domination levels and to preserve the diversity among the optimal set of solutions, respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as convex, non-convex and discrete. This proposed algorithm is also checked for the multi-objective engineering design problems with distinctive features. Furthermore, we show the proposed algorithm effectively generates the Pareto front and is easy to implement and algorithmically simple.

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Acknowledgements

The research of the first author is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). The postdoctoral fellowship of the second author is supported by NSERC.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Faculty of Science, Thompson Rivers University, Kamloops, BC, V2C 0C8, Canada

    Mohamed A. Tawhid & Vimal Savsani

  2. Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Moharam Bey, Alexandria, Egypt

    Mohamed A. Tawhid

  3. Department of Mechanical Engineering, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India

    Vimal Savsani

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  1. Mohamed A. Tawhid
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  2. Vimal Savsani
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Correspondence to Mohamed A. Tawhid.

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Tawhid, M.A., Savsani, V. Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Comput & Applic 31 (Suppl 2), 915–929 (2019). https://doi.org/10.1007/s00521-017-3049-x

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