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Operational Research

, Volume 18, Issue 3, pp 841–863 | Cite as

Vitality-based elephant search algorithm

  • Zhonghuan Tian
  • Simon FongEmail author
  • Suash Deb
  • Rui Tang
  • Raymond Wong
Original Paper
  • 92 Downloads

Abstract

Elephant search algorithm (ESA) is one of the contemporary meta-heuristic search algorithms recently proposed. The male elephants are responsible for global exploration, roaming to new dimensions of search space. The female elephants focus on doing local search, for finding the optimal solution. A lifespan mechanism is designed to control the birth and death that all agents will have an increasing dead probability with their aging incrementally. This mechanism is set to avoid whole agents falling into local optimum and those new-born elephants will evolve by inheriting heuristic information from the ancestors. In the naïve version of ESA, the search agents expire at equal probability regardless of their current locations. It is supposed that search agents who have shown to improve their solutions are more likely to continue producing better results than those mediocre agents. By this concept, a vitality-based elephant search algorithm called VESA is proposed to fine-tune the lifespan of search agents using a vitality computation mechanism that rewards the good performing agents’ longer life at the expense of the mediocre agents. With the lifespan extended, the fit agents have more time to continue enhancing the solutions. Computer simulation on nine testing functions shows the VESA outperforms the naïve ESA in terms of the final fitness value. A min–max based self-adaptive ratio search strategy is also proposed to help find a good gender ratio in a reasonable time.

Keywords

Elephant search algorithm Vitality Meta-heuristic Min–max strategy 

Notes

Acknowledgment

The authors are thankful for the financial supports from the Research Grants titled: “Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF)”, Grant No. MYRG2015-00128-FST; “Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance”, Grant No. MYRG2016-00069-FST; and “A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel”, Grant no. FDCT/126/2014/A3, offered by the University of Macau, and FDCT or Macau SAR government respectively.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Zhonghuan Tian
    • 1
  • Simon Fong
    • 1
    Email author
  • Suash Deb
    • 2
  • Rui Tang
    • 1
  • Raymond Wong
    • 3
  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaMacau SAR
  2. 2.IT and Educational ConsultantRanchiIndia
  3. 3.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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