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Nature-Inspired Metaheuristics in Cloud: A Review

  • Preeti AbrolEmail author
  • Savita Guupta
  • Sukhwinder Singh
Conference paper
  • 41 Downloads
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Metaheuristic Particle swarm optimization (PSO) Ant colony optimization (ACO) Artificial bee colony (ABC) Firefly algorithm Genetic algorithm 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.CDACMohaliIndia
  2. 2.UIETChandigarhIndia

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