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

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)319-335
Number of pages17
JournalNeural Computing and Applications
Issue number8
Publication statusPublished - 1 Apr 2018
Externally publishedYes


  • Chaotic maps
  • Crow Search Algorithm
  • Evolutionary algorithms
  • Multi-Objective optimization

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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