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An enhanced moth flame optimization

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Abstract

Moth flame optimization (MFO) is a recent nature-inspired algorithm, motivated from the transverse orientation of moths in nature. The transverse orientation is a special kind of navigation method, which demonstrates the movement of moths toward moon in a straight path. This algorithm has been successfully applied on various optimization problems. But, MFO suffers from the problem of poor exploration. So, in order to enhance the performance of MFO, some modifications are proposed. A Cauchy distribution function is added to enhance the exploration capability, influence of best flame has been added to improve the exploitation and adaptive step size and division of iterations is followed to maintain a balance between the exploration and exploitation. The proposed algorithm has been named as enhanced moth flame optimization (E-MFO) and to validate the applicability of E-MFO, and it has been applied to twenty benchmark functions. Also, comprehensive comparison of E-MFO with other meta-heuristic algorithms like bat algorithm, bat flower pollination, differential evolution, firefly algorithm, genetic algorithm, particle swarm optimization and flower pollination algorithm has been done. Further, the effect of population and dimension size on the performance of MFO and E-MFO has been discussed. The experimental analysis shows the superior performance of E-MFO over other algorithms in terms of convergence rate and solution quality. Also, statistical testing of E-MFO has been done to prove its significance.

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Authors and Affiliations

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

    Komalpreet Kaur, Urvinder Singh & Rohit Salgotra

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  1. Komalpreet Kaur
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  2. Urvinder Singh
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  3. Rohit Salgotra
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Correspondence to Urvinder Singh.

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Kaur, K., Singh, U. & Salgotra, R. An enhanced moth flame optimization. Neural Comput & Applic 32, 2315–2349 (2020). https://doi.org/10.1007/s00521-018-3821-6

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  • DOI: https://doi.org/10.1007/s00521-018-3821-6

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