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Effect of parametric enhancements on naked mole-rat algorithm for global optimization

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Abstract

Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating patterns of NMRs present in nature. The algorithm though is very simple and linear in nature but suffers from poor exploration during the initial stages and poor exploitation towards the end. Thus to overcome these problems and estimate the effect of basic parameters of NMRA, six new inertia weight strategies and five new mutation operators have been employed. After careful investigation, a new Lévy mutated NMRA (LNMRA) is proposed. The new algorithm employs combined properties of inertia weights and mutation operators altogether. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes and testing is done on CEC 2005, CEC 2014 benchmark problems and real world optimization problem of dual band-notched ultra-wideband (UWB) antenna design. Experimental and statistical results show that the proposed LNMRA is better with respect to other algorithms under comparison.

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Authors and Affiliations

  1. Department of ECE, Thapar Institute of Engineering and Technology, Patiala, India

    Gurdeep Singh & Urvinder Singh

  2. School of Mechanical Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel

    Rohit Salgotra

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  1. Gurdeep Singh
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Singh, G., Singh, U. & Salgotra, R. Effect of parametric enhancements on naked mole-rat algorithm for global optimization. Engineering with Computers 38, 3351–3379 (2022). https://doi.org/10.1007/s00366-021-01344-4

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