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Moth Flame Optimization: Developments and Challenges up to 2020

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Computational Intelligence in Pattern Recognition

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1120))

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Abstract

Nature-inspired algorithms are the current state-of-the-art optimization algorithms which are quite popular due to their high employability and hence became powerful algorithms for solving unique problems of many research areas. These algorithms have been categorized into swarm intelligence, evolutionary as well as others, etc. On the basis of problem type, these algorithms have been applied to solve and to cope up with such type of complex problems. Many algorithms were simulated and inspired by nature as well as proved to be efficient are mostly swarm intelligence-based algorithms such as ACO, ABC and PSO. Also, novel algorithms are being developed and introduced day by day. Out of such developed algorithms, moth flame optimization (MFO) has gained a wider level popularity due to its significant applicability. In this paper, a comprehensive analysis is made on the applicability of MFO as well as its variants by considering the research related to MFO from its initiation up to the year 2020. The major aim of this survey is to motivate the researchers of optimization community to use MFO for solving unapplied problems.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), K Kotturu, Tekkali, Srikakulam, 532201, Andhra Pradesh, India

    Janmenjoy Nayak

  2. Sri Sivani College of Engineering, Srikakulam, 532410, Andhra Pradesh, India

    Kanithi Vakula & Paidi Dinesh

  3. Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Bighnaraj Naik

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  1. Janmenjoy Nayak
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  2. Kanithi Vakula
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Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Applications, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Department of Computer Science and Application, Institute of Engineering and Management (IEM), Kolkata, West Bengal, India

    Soumi Dutta

  5. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Nayak, J., Vakula, K., Dinesh, P., Naik, B. (2020). Moth Flame Optimization: Developments and Challenges up to 2020. In: Das, A., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-2449-3_40

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