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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2019

12.06.2017 | Original Article

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

verfasst von: Mohamed A. Tawhid, Vimal Savsani

Erschienen in: Neural Computing and Applications | Sonderheft 2/2019

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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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Metadaten
Titel
Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems
verfasst von
Mohamed A. Tawhid
Vimal Savsani
Publikationsdatum
12.06.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3049-x

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