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Nature-Inspired Metaheuristics in Cloud: A Review

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ICT Systems and Sustainability

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1077))

Abstract

Due to the successful deployment and high performance, metaheuristic review is extensively surveyed in the literature that includes algorithms, their comparisons, and analysis along with its applications. Although, insightful performance analysis of metaheuristic is done by few researchers still it is a “black box”. The performance analysis of algorithms is performed. This paper addresses an extensive review of four nature-inspired metaheuristics, namely, ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO), firefly algorithm, and genetic algorithm. It includes introduction to algorithms, its modifications and variants, analysis, comparisons, research gaps, and future work. Highlighting the potential and critical issues are the main objective of intensive research. The metaheuristic review provides insight for future research work.

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Acknowledgements

The author thank Mr. Sukhwinder Singh for his support, help, and guidance. We extend our gratitude toward for sparing his valuable time in helping us.

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Authors and Affiliations

  1. CDAC, Mohali, India

    Preeti Abrol

  2. UIET, Chandigarh, India

    Savita Guupta & Sukhwinder Singh

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  1. Preeti Abrol
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    Milan Tuba

  2. ITM University, Gwalior, India

    Shyam Akashe

  3. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Abrol, P., Guupta, S., Singh, S. (2020). Nature-Inspired Metaheuristics in Cloud: A Review. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_2

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