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100			_aAishwarya Mishra
245			_aMetaheuristic Algorithms in Smart Farming: An Analytical Survey
300			_ap46-65
520			_aThe 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.
654			_aNature inspired algorithms _aOptimization algorithms _aEvolutionary algorithms _aSmart farming
700			_aLavika Goel
773	0		_080270 _9109582 _dNew Delhi IETE _oJP25 _tIETE Technical Review _x0256-4602
942			_cJA
942			_2ddc
999			_c130132 _d130132