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A Conceptual Comparison of Dragonfly Algorithm Variants for CEC-2021 Global Optimization Problems

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Abstract

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder–Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems.

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Authors and Affiliations

  1. Mukesh Patel School of Technology Management and Engineering, SVKM’s Narsee Monjee Institute of Management Studies, Shirpur Campus, Dhule, Maharashtra, India

    Milan Joshi

  2. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita

  3. Rajasthan Rajya Vidyut Prasaran Nigam, Losal, Sikar, Rajasthan, India

    Pradeep Jangir

  4. Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

    Iman Ahmadianfar

  5. Department of Production Engineering, Jadavpur University, Kolkata, India

    Shankar Chakraborty

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  1. Milan Joshi
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  2. Kanak Kalita
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  3. Pradeep Jangir
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  4. Iman Ahmadianfar
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  5. Shankar Chakraborty
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Correspondence to Shankar Chakraborty.

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Joshi, M., Kalita, K., Jangir, P. et al. A Conceptual Comparison of Dragonfly Algorithm Variants for CEC-2021 Global Optimization Problems. Arab J Sci Eng 48, 1563–1593 (2023). https://doi.org/10.1007/s13369-022-06880-9

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  • DOI: https://doi.org/10.1007/s13369-022-06880-9

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