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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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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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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