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Comparative Analysis of Multi-objective Metaheuristic Algorithms by Means of Performance Metrics to Continuous Problems

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

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Abstract

Multi-objective problems (MOP) are an important type of real-world problem that involves more than one objective that must be optimized simultaneously. There exists a wide variety of metaheuristic algorithms aimed to work on that type of problems, however, the selection of which algorithm should be used to a given MOP depends on the expertise of the researcher. This decision is not straightforward and usually means to use extra computational resources to try different multi-objective metaheuristics even before to try to solve the interested domain. In the state of the art, there exists several metrics to compare and contrasts the performance of two or more given multi-objective algorithms. In this work, we use these metrics to compare a set of well-known multi-objective metaheuristics over the continuous CEC 2009 benchmark with the objective to give the interested researcher useful information to properly select a multi-objective algorithm.

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Acknowledgements

Authors express their gratitude to the Universidad de Guanajuato (UG) for its support provided to carry out the present research.

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Authors and Affiliations

  1. División de Ciencias Económico-Administrativas, Departamento de Estudios Organizacionales, Universidad de Guanajuato, Guanajuato, Mexico

    Carlos Juarez-Santini, J. A. Soria-Alcaraz, M. A. Sotelo-Figueroa & Esteves-Jiménez Velino

Authors
  1. Carlos Juarez-Santini
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  2. J. A. Soria-Alcaraz
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  3. M. A. Sotelo-Figueroa
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  4. Esteves-Jiménez Velino
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Corresponding author

Correspondence to J. A. Soria-Alcaraz .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Juarez-Santini, C., Soria-Alcaraz, J.A., Sotelo-Figueroa, M.A., Velino, EJ. (2020). Comparative Analysis of Multi-objective Metaheuristic Algorithms by Means of Performance Metrics to Continuous Problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_37

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