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Efficient chaotic imperialist competitive algorithm with dropout strategy for global optimization

Symmetry, ISSN: 2073-8994, Vol: 12, Issue: 4, Page: 1-16
2020
  • 8
    Citations
  • 0
    Usage
  • 26
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    8
    • Citation Indexes
      8
  • Captures
    26

Article Description

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs' path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

Bibliographic Details

Zong Sheng Wang; Jung Lee; Chang Geun Song; Sun Jeong Kim

MDPI AG

Computer Science; Chemistry; Mathematics; Physics and Astronomy

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