Vitality-based elephant search algorithm

Zhonghuan Tian (), Simon Fong (), Suash Deb (), Rui Tang () and Raymond Wong ()
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Zhonghuan Tian: University of Macau
Simon Fong: University of Macau
Suash Deb: IT and Educational Consultant
Rui Tang: University of Macau
Raymond Wong: University of New South Wales

Operational Research, 2018, vol. 18, issue 3, No 14, 863 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12351-018-0419-9

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