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The naked mole-rat algorithm

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Abstract

This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm.

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Acknowledgements

Rohit Salgotra acknowledges the support of INSPIRE Fellowship (IF-160215) by Directorate of Science and Technology, Govt. of India.

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  1. Department of ECE, Thapar University, Patiala, Punjab, India

    Rohit Salgotra & Urvinder Singh

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  1. Rohit Salgotra
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Correspondence to Rohit Salgotra.

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Salgotra, R., Singh, U. The naked mole-rat algorithm. Neural Comput & Applic 31, 8837–8857 (2019). https://doi.org/10.1007/s00521-019-04464-7

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