@incollection{chakraborty_swarm_2017,
	address = {Cham},
	series = {Modeling and {Optimization} in {Science} and {Technologies}},
	title = {Swarm {Intelligence}: {A} {Review} of {Algorithms}},
	isbn = {978-3-319-50920-4},
	shorttitle = {Swarm {Intelligence}},
	url = {https://doi.org/10.1007/978-3-319-50920-4_19},
	abstract = {Swarm intelligence (SI), an integral part in the field of artificial intelligence, is gradually gaining prominence, as more and more high complexity problems require solutions which may be sub-optimal but yet achievable within a reasonable period of time. Mostly inspired by biological systems, swarm intelligence adopts the collective behaviour of an organized group of animals, as they strive to survive. This study aims to discuss the governing idea, identify the potential application areas and present a detailed survey of eight SI algorithms. The newly developed algorithms discussed in the study are the insect-based algorithms and animal-based algorithms in minute detail. More specifically, we focus on the algorithms inspired by ants, bees, fireflies, glow-worms, bats, monkeys, lions and wolves. The inspiration analyses on these algorithms highlight the way these algorithms operate. Variants of these algorithms have been introduced after the inspiration analysis. Specific areas for the application of such algorithms have also been highlighted for researchers interested in the domain. The study attempts to provide an initial understanding for the exploration of the technical aspects of the algorithms and their future scope by the academia and practice.},
	language = {en},
	urldate = {2021-11-11},
	booktitle = {Nature-{Inspired} {Computing} and {Optimization}: {Theory} and {Applications}},
	publisher = {Springer International Publishing},
	author = {Chakraborty, Amrita and Kar, Arpan Kumar},
	editor = {Patnaik, Srikanta and Yang, Xin-She and Nakamatsu, Kazumi},
	year = {2017},
	doi = {10.1007/978-3-319-50920-4_19},
	keywords = {Bio-inspired algorithms, Intelligent algorithms, Literature review, Machine learning, Nature-inspired computing, Swarm intelligence},
	pages = {475--494},
}

{"_id":"oTQQjrm3Xfz4fpcJA","bibbaseid":"chakraborty-kar-swarmintelligenceareviewofalgorithms-2017","author_short":["Chakraborty, A.","Kar, A. K."],"bibdata":{"bibtype":"incollection","type":"incollection","address":"Cham","series":"Modeling and Optimization in Science and Technologies","title":"Swarm Intelligence: A Review of Algorithms","isbn":"978-3-319-50920-4","shorttitle":"Swarm Intelligence","url":"https://doi.org/10.1007/978-3-319-50920-4_19","abstract":"Swarm intelligence (SI), an integral part in the field of artificial intelligence, is gradually gaining prominence, as more and more high complexity problems require solutions which may be sub-optimal but yet achievable within a reasonable period of time. Mostly inspired by biological systems, swarm intelligence adopts the collective behaviour of an organized group of animals, as they strive to survive. This study aims to discuss the governing idea, identify the potential application areas and present a detailed survey of eight SI algorithms. The newly developed algorithms discussed in the study are the insect-based algorithms and animal-based algorithms in minute detail. More specifically, we focus on the algorithms inspired by ants, bees, fireflies, glow-worms, bats, monkeys, lions and wolves. The inspiration analyses on these algorithms highlight the way these algorithms operate. Variants of these algorithms have been introduced after the inspiration analysis. Specific areas for the application of such algorithms have also been highlighted for researchers interested in the domain. The study attempts to provide an initial understanding for the exploration of the technical aspects of the algorithms and their future scope by the academia and practice.","language":"en","urldate":"2021-11-11","booktitle":"Nature-Inspired Computing and Optimization: Theory and Applications","publisher":"Springer International Publishing","author":[{"propositions":[],"lastnames":["Chakraborty"],"firstnames":["Amrita"],"suffixes":[]},{"propositions":[],"lastnames":["Kar"],"firstnames":["Arpan","Kumar"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Patnaik"],"firstnames":["Srikanta"],"suffixes":[]},{"propositions":[],"lastnames":["Yang"],"firstnames":["Xin-She"],"suffixes":[]},{"propositions":[],"lastnames":["Nakamatsu"],"firstnames":["Kazumi"],"suffixes":[]}],"year":"2017","doi":"10.1007/978-3-319-50920-4_19","keywords":"Bio-inspired algorithms, Intelligent algorithms, Literature review, Machine learning, Nature-inspired computing, Swarm intelligence","pages":"475–494","bibtex":"@incollection{chakraborty_swarm_2017,\n\taddress = {Cham},\n\tseries = {Modeling and {Optimization} in {Science} and {Technologies}},\n\ttitle = {Swarm {Intelligence}: {A} {Review} of {Algorithms}},\n\tisbn = {978-3-319-50920-4},\n\tshorttitle = {Swarm {Intelligence}},\n\turl = {https://doi.org/10.1007/978-3-319-50920-4_19},\n\tabstract = {Swarm intelligence (SI), an integral part in the field of artificial intelligence, is gradually gaining prominence, as more and more high complexity problems require solutions which may be sub-optimal but yet achievable within a reasonable period of time. Mostly inspired by biological systems, swarm intelligence adopts the collective behaviour of an organized group of animals, as they strive to survive. This study aims to discuss the governing idea, identify the potential application areas and present a detailed survey of eight SI algorithms. The newly developed algorithms discussed in the study are the insect-based algorithms and animal-based algorithms in minute detail. More specifically, we focus on the algorithms inspired by ants, bees, fireflies, glow-worms, bats, monkeys, lions and wolves. The inspiration analyses on these algorithms highlight the way these algorithms operate. Variants of these algorithms have been introduced after the inspiration analysis. Specific areas for the application of such algorithms have also been highlighted for researchers interested in the domain. The study attempts to provide an initial understanding for the exploration of the technical aspects of the algorithms and their future scope by the academia and practice.},\n\tlanguage = {en},\n\turldate = {2021-11-11},\n\tbooktitle = {Nature-{Inspired} {Computing} and {Optimization}: {Theory} and {Applications}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Chakraborty, Amrita and Kar, Arpan Kumar},\n\teditor = {Patnaik, Srikanta and Yang, Xin-She and Nakamatsu, Kazumi},\n\tyear = {2017},\n\tdoi = {10.1007/978-3-319-50920-4_19},\n\tkeywords = {Bio-inspired algorithms, Intelligent algorithms, Literature review, Machine learning, Nature-inspired computing, Swarm intelligence},\n\tpages = {475--494},\n}\n\n","author_short":["Chakraborty, A.","Kar, A. 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