Metaheuristic Algorithms in Smart Farming: An Analytical Survey

AbstractThe techniques for solving complex optimization problems using nature inspired metaheuristic algorithms are widely accepted. Nature inspired methods use nature derived approaches to offer an efficient solution within polynomial time. This paper presents analytics of some of the significant nature inspired metaheuristic algorithms. It elaborates on the principles and concepts that are used in these algorithms representing their similarities, variations, and exceptions. The taxonomical classification and analytics presented in this paper list the nature derived phenomenon used to develop a wide variety of nature-inspired techniques. The algorithms are classified as per the type of agents used, search techniques, sub-optimization methods, type of constraints, and nature of problems. The survey comprehends the use of control parameters like exploration and convergence applicable to these algorithms and their domain specifications. The sources of nature inspiration are also presented with their variants. It establishes the analytics required to choose a specific nature-inspired heuristic algorithm for smart farming and related applications. Metaheuristic algorithms like Particle Swarm optimization, Ant colony optimization, Whale optimization, Firefly optimization, etc. have contributed significantly in assisting smart farming methods for better productivity of crops.KEYWORDS: Nature inspired algorithmsOptimization algorithmsEvolutionary algorithmsSmart farming ACKNOWLEDGEMENTSThe authors like to express their sincere gratitude to the Department of Science and Technology, Science and Engineering Research Board (SERB) under the Core Research Grant Scheme (File no. DST/CRG/2022/000472 and sanction order SERB/F/10778/2022-2023) for giving financial support at the Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India.Notes on contributorsAishwarya MishraAishwarya Mishra received her bachelor of technology in Computer Science & Engineering from I.T.M. Gorakhpur, Uttar Pradesh, India in 2017, and master of technology in Information Technology from Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India in 2019. She earlier worked at G L Bajaj Institute of Technology (GLBITM), Greater Noida from 2019 to 2020 and currently pursuing PhD from Malaviya National Institute of Technology (MNIT) Jaipur Rajasthan, India. Her area of interest is Artificial intelligence, Evolutionary computations, Nature inspired intelligence, and optimization algorithms. At present, she is working on crop recommendation system using Geoscience based nature inspired algorithm. Corresponding author. Email: aishwaryamishra1609@gmail.comLavika GoelLavika Goel received her bachelor of technology degree in computer science & engineering from JSSATE, UP technical University, Noida, India in 2008 and master of engineering. in computer tech & applications, from Delhi College of Engineering, Delhi University, Delhi, India in 2010, and doctor of philosophy in (computer engineering) from Delhi Technological University, Delhi, India in 2015. She is currently an assistant professor in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (NIT), Jaipur, India. She earlier worked at Birla Institute of Technology and Science, Pilani during 2015–2020. She holds corporate experience working at Oracle India Private Ltd. Her research areas include nature inspired intelligence, optimization algorithms, machine learning, hybrid intelligent systems, soft computing, remote sensing, and satellite image processing. Dr Goel has published around 80 research papers in SCI-indexed international conferences, journals and book chapters including Elsevier, IEEE, Taylor and Francis, Springer, etc. She has written a text book on Artificial Intelligence for undergraduate students which was published in January 2021 by Wiley Publications. This book is amongst the top 10 bestseller books of Artificial Intelligence globally on the Amazon website since May 2021 – till date. She received in August 2019 and in October 2022, external fundings of Rs. 40 lakhs as Co-Principal Investigator and Rs. 30 lakhs as Principal Investigator from Department of Science and Technology (DST), Interdisciplinary cyber physical systems division and SERB-Core Research Grant Scheme respectively for her novel idea of using nature inspired heuristics for effectively developing a recommender system for answering web-based user queries and for developing a crop recommender system. She is also a life member of Institution of Engineers India Ltd. (IEI), India, and machine intelligence and research labs (MIR), Washington, USA. She has been elevated to the grade of senior member of IEEE in November, 2020. She was awarded the prestigious “Young Scientist Award” by VIFRA International Foundation in 2015. She is also the Joint Winner of the IEEE India “Best Women Profe ssional” award given by IEEE India Council for Dr Prahlad P Chhabria award, 2021. This achievement of hers got featured as an article in the Dainik Bhaskar newspaper on 20th Jan 2022. Email: lavika.cse@mnit.ac.in