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Swarm Intelligence

In: Search and Optimization by Metaheuristics

Author

Listed:
  • Ke-Lin Du

    (Xonlink Inc
    Concordia University, Department of Electrical and Computer Engineering)

  • M. N. S. Swamy

    (Concordia University, Department of Electrical and Computer Engineering)

Abstract

Nature-inspired optimization algorithms can, generally, be grouped into evolutionary approaches and swarm intelligence methods. EAs try to improve the candidate solutions (chromosomes) using evolutionary operators. Swarm intelligence methods use differential position update rules for obtaining new candidate solutions. The popularity of the swarm intelligence methods is due to their simplicity, easy adaptation to the problem, and effectiveness in solving the complex optimization problems.

Suggested Citation

  • Ke-Lin Du & M. N. S. Swamy, 2016. "Swarm Intelligence," Springer Books, in: Search and Optimization by Metaheuristics, chapter 0, pages 237-263, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-41192-7_15
    DOI: 10.1007/978-3-319-41192-7_15
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