Skip to main content
Studies in Computational Intelligence

Hybrid metaheuristic algorithms: Past, present, and future

Publication ,  Chapter
Ting, TO; Yang, XS; Cheng, S; Huang, K
January 1, 2015

Hybrid algorithms play a prominent role in improving the search capability of algorithms. Hybridization aims to combine the advantages of each algorithm to form a hybrid algorithm, while simultaneously trying to minimize any substantial disadvantage. In general, the outcome of hybridization can usually make some improvements in terms of either computational speed or accuracy. This chapter surveys recent advances in the area of hybridizing different algorithms. Based on this survey, some crucial recommendations are suggested for further development of hybrid algorithms.

Duke Scholars

DOI

Publication Date

January 1, 2015

Volume

585

Start / End Page

71 / 83

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4602 Artificial intelligence
  • 4007 Control engineering, mechatronics and robotics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ting, T. O., Yang, X. S., Cheng, S., & Huang, K. (2015). Hybrid metaheuristic algorithms: Past, present, and future. In Studies in Computational Intelligence (Vol. 585, pp. 71–83). https://doi.org/10.1007/978-3-319-13826-8_4
Ting, T. O., X. S. Yang, S. Cheng, and K. Huang. “Hybrid metaheuristic algorithms: Past, present, and future.” In Studies in Computational Intelligence, 585:71–83, 2015. https://doi.org/10.1007/978-3-319-13826-8_4.
Ting TO, Yang XS, Cheng S, Huang K. Hybrid metaheuristic algorithms: Past, present, and future. In: Studies in Computational Intelligence. 2015. p. 71–83.
Ting, T. O., et al. “Hybrid metaheuristic algorithms: Past, present, and future.” Studies in Computational Intelligence, vol. 585, 2015, pp. 71–83. Scopus, doi:10.1007/978-3-319-13826-8_4.
Ting TO, Yang XS, Cheng S, Huang K. Hybrid metaheuristic algorithms: Past, present, and future. Studies in Computational Intelligence. 2015. p. 71–83.

DOI

Publication Date

January 1, 2015

Volume

585

Start / End Page

71 / 83

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4602 Artificial intelligence
  • 4007 Control engineering, mechatronics and robotics