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Improving multi-criterion optimization with chaos: a novel Multi-Objective Chaotic Crow Search Algorithm

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Abstract

This paper presents two multi-criteria optimization techniques: the Multi-Objective Crow Search Algorithm (MOCSA) and an improved chaotic version called Multi-Objective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Crow Search Algorithm. Crows are intelligent animals with interesting strategies for protecting their food hatches. This compelling behavior is extended into a Multi-Objective approach. MOCCSA uses chaotic-based criteria on the optimization process to improve the diversity of solutions. To determinate if the performance of the algorithm is significantly enhanced, the incorporation of a chaotic operator is further validated by a statistical comparison between the proposed MOCCSA and its chaotic-free counterpart (MOCSA) indicating that the results of the two algorithms are significantly different from each other. The performance of MOCCSA is evaluated by a set of standard benchmark functions, and the results are contrasted with two well-known algorithms: Multi-Objective Dragonfly Algorithm and Multi-Objective Particle Swarm Optimization. Both quantitative and qualitative results show competitive results for the proposed approach.

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  • 02 December 2017

    In the original publication, Algorithm 1 and Algorithm 2 are incorrectly published with the same content.

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Acknowledgments

The second author acknowledges The National Council of Science and Technology of Mexico (CONACyT) for the doctoral Grant number 298285 for supporting this research.

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Authors and Affiliations

  1. Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, 28040, Madrid, Spain

    Salvador Hinojosa & Gonzalo Pajares

  2. División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico

    Diego Oliva, Erik Cuevas, Omar Avalos & Jorge Gálvez

Authors
  1. Salvador Hinojosa
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  2. Diego Oliva
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  3. Erik Cuevas
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  4. Gonzalo Pajares
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  5. Omar Avalos
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  6. Jorge Gálvez
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Corresponding author

Correspondence to Diego Oliva.

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This research was partially supported by The National Council of Science and Technology of Mexico (CONACyT) for the doctoral Grant number 298285.None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.

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It is to specifically state that “No Competing interests are at stake and there is No Conflict of Interest” with other people or organizations that could inappropriately influence or bias the content of the paper.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

The original version of this article was revised: Algorithm 1 and Algorithm 2 were incorrectly published with the same content. Now, it has been corrected.

A correction to this article is available online at https://doi.org/10.1007/s00521-017-3293-0.

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Hinojosa, S., Oliva, D., Cuevas, E. et al. Improving multi-criterion optimization with chaos: a novel Multi-Objective Chaotic Crow Search Algorithm. Neural Comput & Applic 29, 319–335 (2018). https://doi.org/10.1007/s00521-017-3251-x

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