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Neural Computing and Applications

, Volume 29, Issue 8, pp 319–335 | Cite as

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

  • Salvador Hinojosa
  • Diego Oliva
  • Erik Cuevas
  • Gonzalo Pajares
  • Omar Avalos
  • Jorge Gálvez
Original Article
  • 329 Downloads

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.

Keywords

Multi-Objective optimization Evolutionary algorithms Chaotic maps Crow Search Algorithm 

Notes

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.

Compliance with ethical standards

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.

Conflict of interest

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.

Human and animal rights statement

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

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad InformáticaUniversidad Complutense de MadridMadridSpain
  2. 2.División de Electrónica y ComputaciónUniversidad de Guadalajara, CUCEIGuadalajaraMexico

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