Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social n...

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Published in:	The Artificial intelligence review Vol. 53; no. 3; pp. 1737 - 1765
Main Authors:	Deb, Sanchari, Gao, Xiao-Zhi, Tammi, Kari, Kalita, Karuna, Mahanta, Pinakeswar
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.03.2020 Springer Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Biology Birds Computer Science Distributed generation Feature selection Food Foraging behavior Genetic algorithms Heuristic Heuristic methods Mathematical optimization Mechanical engineering Meta-analysis Optimization Optimization algorithms Poultry Route planning Social networks Unit commitment Variants Vehicle routing Optimization algorithm Review Nature inspired intelligence Applications Chicken Swarm Optimization algorithm
ISSN:	0269-2821, 1573-7462
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Abstract	Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.
AbstractList	Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.
Audience	Academic
Author	Mahanta, Pinakeswar Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna
Author_xml	– sequence: 1 givenname: Sanchari surname: Deb fullname: Deb, Sanchari email: sancharideb@yahoo.co.in organization: Centre for Energy, Indian Institute of Technology – sequence: 2 givenname: Xiao-Zhi surname: Gao fullname: Gao, Xiao-Zhi organization: School of Computing, University of Eastern Finland – sequence: 3 givenname: Kari surname: Tammi fullname: Tammi, Kari organization: Department of Mechanical Engineering, Aalto University – sequence: 4 givenname: Karuna surname: Kalita fullname: Kalita, Karuna organization: Department of Mechanical Engineering, Indian Institute of Technology – sequence: 5 givenname: Pinakeswar surname: Mahanta fullname: Mahanta, Pinakeswar organization: Department of Mechanical Engineering, Indian Institute of Technology
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Cites_doi	10.1109/CompComm.2017.8322928 10.1145/2480741.2480752 10.1088/1755-1315/69/1/012086 10.1109/IHMSC.2016.107 10.1109/CIS.2017.00052 10.1155/2015/258491 10.1016/j.compstruc.2014.03.007 10.1109/ROEDUNET.2018.8514152 10.1080/0952813X.2015.1042530 10.1504/IJBIC.2010.032124 10.1061/(ASCE)AS.1943-5525.0000757 10.1109/ICEEIE.2017.8328774 10.1007/978-3-319-11857-4_10 10.1016/j.applanim.2016.08.010 10.1109/PCITC.2015.7438173 10.1063/1.4982562 10.1504/IJBIC.2016.078666 10.1016/j.tcs.2005.05.020 10.1007/s11721-007-0002-0 10.1109/SOCPAR.2015.7492775 10.3390/en11010178 10.1049/iet-map.2016.0083 10.1016/j.advengsoft.2013.12.007 10.1109/JSEN.2018.2832216 10.1016/j.future.2017.08.060 10.1109/ICRCICN.2017.8234486 10.1109/CYBER.2015.7288023 10.1016/j.energy.2016.05.128 10.1504/IJBIC.2016.081335 10.1016/j.cnsns.2012.05.010 10.4018/978-1-5225-2229-4.ch047 10.1142/S0218213008003893 10.1109/TEVC.2010.2059031 10.1007/s11227-018-2291-z 10.1109/ICCSN.2017.8230156 10.1109/ACCESS.2016.2604738 10.1109/ACCESS.2017.2783969 10.1109/MLDS.2017.5 10.1016/j.asoc.2007.05.007 10.1007/s10071-016-1064-4 10.1007/978-3-642-32894-7_27 10.1080/01430750.2017.1345010 10.1016/S0377-2217(99)00435-X 10.1109/ICRCICN.2017.8234517 10.1023/A:1022602019183 10.3390/su8030235 10.1016/j.eswa.2015.04.026 10.1109/AINA.2018.00073 10.1109/CompComm.2016.7925083 10.1016/j.neucom.2015.08.092 10.1007/978-3-319-74690-6_6 10.1007/s11277-017-4803-1 10.1007/978-3-319-61833-3_2
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References	Pei Y, Hao J (2017) Non-dominated sorting and crowding distance based multi-objective chaotic evolution. In: International conference in swarm intelligence. Springer, Cham, pp 15–22 Zareiegovar G, Fesaghandis RR, Azad MJ (2012) Optimal DG location and sizing in distribution system to minimize losses, improve voltage stability, and voltage profile. In: Proceedings of 17th conference on electrical power distribution networks (EPDC), pp 1–6 ShayokhMShinSYBio inspired distributed WSN localization based on Chicken Swarm OptimizationWireless Pers Commun20179745691570610.1007/s11277-017-4803-1 Wang Q, Zhu L (2017) Optimization of wireless sensor networks based on chicken swarm optimization algorithm. In: AIP conference proceedings, vol 1839, no 1. AIP Publishing, p 020197 Hafez AI, Zawbaa HM, Emary E, Mahmoud HA, Hassanien AE (2015) An innovative approach for feature selection based on chicken swarm optimization. In: 2015 7th international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 19–24 MengXBLiHXGaoXZAn adaptive reinforcement learning-based bat algorithm for structural design problemsInt J Bio Inspir Comput2018111(in press) ChenYLHePLZhangYHCombining penalty function with modified chicken swarm optimization for constrained optimizationAdv Intell Syst Res201512618991907 Abbas Z, Javaid N, Khan AJ, Rehman MHA, Sahi J, Saboor A (2018) Demand side energy management using hybrid chicken swarm and bacterial foraging optimization techniques. In: 2018 IEEE 32nd international conference on advanced information networking and applications (AINA), IEEE, pp 445–456 LiYWuYQuXChicken Swarm-based method for ascent trajectory optimization of hypersonic vehiclesJ Aerosp Eng20173050401704310.1061/(ASCE)AS.1943-5525.0000757 Deb S, Kalita K, Gao XZ, Tammi K, Mahanta P (2017) Optimal placement of charging stations using CSO-TLBO algorithm. In: 2017 Third international conference on research in computational intelligence and communication networks (ICRCICN). IEEE, pp 84–89 ChenSYangRYangRYangLYangXXuCLiuWA parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimizationDiscrete Dyn Nat Soc2016201611 WangGGDebSGaoXZCoelhoLDSA new metaheuristic optimisation algorithm motivated by elephant herding behaviourInt J Bio-Inspir Comput20168639440910.1504/IJBIC.2016.081335 MengXBGaoXZLiuYZhangHA novel bat algorithm with habitat selection and Doppler effect in echoes for optimizationExpert Syst Appl20154217–186350636410.1016/j.eswa.2015.04.026 Poli R, Langdon WB (1998) On the search properties of different crossover operators in genetic programming. In: Genetic programming 1998: proceedings of third annual conference, University of Wisconsin, Madison. Morgan Kaufmann, pp 293–301 Moldovan D, Chifu V, Pop C, Cioara T, Anghel I, Salomie I (2018) Chicken Swarm Optimization and deep learning for manufacturing processes. In: 2018 17th RoEduNet conference: networking in education and research (RoEduNet). IEEE, pp 1–6 Yi Z, Liu J, Wang S, Zeng X, Lu J (2016) PAPR reduction technology based on CSO algorithm in CO-OFDM system. In: 2016 15th international conference on optical communications and networks (ICOCN). IEEE, pp 1–3 SultanaUKhairuddinABMokhtarASZareenNSultanaBGrey wolf optimizer based placement and sizing of multiple distributed generation in the distribution systemEnergy201611152553610.1016/j.energy.2016.05.128 KarabogaDBasturkBOn the performance of artificial bee colony (ABC) algorithmAppl Soft Comput20088168769710.1016/j.asoc.2007.05.007 DasSSuganthanPNDifferential evolution: a survey of the state-of-the-artIEEE Trans Evol Comput201115143110.1109/TEVC.2010.2059031 GandomiAHAlaviAHKrill herd: a new bio-inspired optimization algorithmCommun Nonlinear Sci Numer Simul201217124831484529602791266.6509210.1016/j.cnsns.2012.05.010 MengXBGaoXZLuLLiuYZhangHA new bio-inspired optimisation algorithm: bird swarm algorithmJ Exp Theor Artif Intell201628467368710.1080/0952813X.2015.1042530 Meng XB, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: Chicken Swarm Optimization. In: International conference in swarm intelligence. Springer, Cham, pp 86–94 TorabiSSafi-EsfahaniFA dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computingJ Supercomput20187462581262610.1007/s11227-018-2291-z Ahmed K, Hassanien AE, Ezzat E, Bhattacharyya S (2018) Swarming behaviors of chicken for predicting posts on facebook branding pages. In: International conference on advanced machine learning technologies and applications. Springer, Cham, pp 52–61 Han M, Liu S (2017) An improved binary chicken swarm optimization algorithm for solving 0–1 Knapsack problem. In: 2017 13th international conference on computational intelligence and security (CIS). IEEE, pp 207–210 PoliRKennedyJBlackwellTParticle swarm optimizationSwarm Intell200711335710.1007/s11721-007-0002-0 Taie SA, Ghonaim W (2017) CSO-based algorithm with support vector machine for brain tumor’s disease diagnosis. In: 2017 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops). IEEE, pp 183–187 QuCZhaoSAFuYHeWChicken swarm optimization based on elite opposition-based learningMath Probl Eng20172017203633344 DorigoMBlumCAnt colony optimization theory: a surveyTheoret Comput Sci20053442–324327821788551154.9062610.1016/j.tcs.2005.05.020 GoldbergDEHollandJHGenetic algorithms and machine learningMach Learn198832959910.1023/A:1022602019183 Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214 MarinakisYDouniasGNature inspired intelligence in medicine: ant colony optimization for pap-smear diagnosisInt J Artif Intell Tools2008170227930110.1142/S0218213008003893 MohamedTMEnhancing The performance of the greedy algorithm using Chicken Swarm Optimization: an application to exam scheduling problemEgypt Comput Sci J201842113776448 LiuDLiuCFuQLiTKhanMICuiSFaizMAProjection pursuit evaluation model of regional surface water environment based on improved Chicken Swarm Optimization algorithmWater Resour Manag201732118 AhmedKBabersRDarwishAHassanienAEPandaMAbrahamAHassanienAESwarm-based analysis for community detection in complex networksBig data analytics a social network approach2018LondonTaylor and Francis18 Liang S, Feng T, Sun G, Zhang J, Zhang H (2016) Transmission power optimization for reducing sidelobe via bat-chicken swarm optimization in distributed collaborative beamforming. In: 2016 2nd IEEE international conference on computer and communications (ICCC). IEEE, pp 2164–2168 KumarDSVeniSEnhanced energy steady clustering using convergence node based path optimization with hybrid Chicken Swarm algorithm in MANETInt J Pure Appl Math2018118767788 LogeshRSubramaniyaswamyVVijayakumarVGaoXZIndragandhiVA hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart cityFuture Gener Comput Syst20188365367310.1016/j.future.2017.08.060 YangXSFirefly algorithm, stochastic test functions and design optimisationInt J Bio-Inspir Comput201022788410.1504/IJBIC.2010.032124 Awal AR, Dou Z, Al Shayokh M, Zahoor MI (2017) Implementation of chicken swarm optimization (CSO) with partial transmit sequences for the reduction of PAPR in OFDM system. In: 2017 IEEE 9th international conference on communication software and networks (ICCSN). IEEE, pp 468–472 Deb S, Kalita K, Gao XZ, Tammi K, Mahanta P (2018a) A pareto dominance based multi-objective Chicken Swarm Optimization and teaching learning based optimization algorithm for charging station placement problem. Int Trans Electr Energy Syst (to be communicated) HuHLiJHuangJEconomic operation optimization of micro-grid based on Chicken Swarm Optimization algorithmHigh Volt Appar20171020 MohsenzadehAPazoukiSArdalanSHaghifamMROptimal placing and sizing of parking lots including different levels of charging stations in electric distribution networksInt J Ambient Energy201839774375010.1080/01430750.2017.1345010 Wang K, Li Z, Cheng H, Zhang K (2017) Mutation chicken swarm optimization based on nonlinear inertia weight. In: 2017 3rd IEEE international conference on computer and communications (ICCC). IEEE, pp 2206–2211 Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249 Banerjee S, Chattopadhyay S (2015) Improved serially concatenated convolution turbo code (SCCTC) using chicken swarm optimization. In: Power, communication and information technology conference (PCITC), 2015 IEEE. IEEE, pp 268–273 DebSTammiKKalitaKMahantaPImpact of electric vehicle charging station load on distribution networkEnergies201811117810.3390/en11010178 Ahmed K, Hassanien AE, Ezzat E, Tsai PW (2016) An adaptive approach for community detection based on chicken swarm optimization algorithm. In: International conference on genetic and evolutionary computing. Springer, Cham, pp 281–288 IrsalindaNThobirinAWijayantiDEChicken swarm as a multi step algorithm for global optimizationInt J Eng Sci Invent201761814 AhmedKHassanienAEEzzatEHassanienAEGaberTAn efficient approach for community detection in complex social networks based on elephant swarm optimization algorithmHandbook of research on machine learning innovations and trends2017HersheyIGI Global1062107510.4018/978-1-5225-2229-4.ch047 ČrepinšekMLiuSHMernikMExploration and exploitation in evolutionary algorithms: a surveyACM Comput Surv (CSUR)2013453351293.6825110.1145/2480741.2480752 McGrathNBurmanODwyerCPhillipsCJDoes the anticipatory behaviour of chickens communicate reward quality?Appl Anim Behav Sci2016184809010.1016/j.applanim.2016.08.010 Basha SH, Tharwat A, Ahmed K, Hassanien AE (2018) A predictive model for seminal quality using neutrosophic rule-based classification system. In: International conference on advanced intelligent systems and informatics. Springer, Cham, pp 495–504 MishraKKHaritSA fast alg N Irsalinda (9718_CR77) 2017; 6 S Das (9718_CR17) 2011; 15 S Chen (9718_CR14) 2016; 2016 XB Meng (9718_CR46) 2018; 1 9718_CR18 9718_CR19 MY Cheng (9718_CR15) 2014; 139 9718_CR10 9718_CR54 9718_CR11 L Marino (9718_CR39) 2017; 20 D Liu (9718_CR36) 2017; 32 9718_CR57 AH Gandomi (9718_CR23) 2012; 17 K Ahmed (9718_CR5) 2017 9718_CR51 R Logesh (9718_CR37) 2018; 83 9718_CR52 9718_CR53 J Heng (9718_CR28) 2016; 8 N McGrath (9718_CR40) 2016; 184 A Hertz (9718_CR29) 2000; 126 X Cai (9718_CR12) 2016; 8 H Zhang (9718_CR76) 2016; 173 C Qu (9718_CR56) 2017; 2017 XB Meng (9718_CR43) 2015; 42 Y Marinakis (9718_CR38) 2008; 17 YL Chen (9718_CR13) 2015; 126 TM Mohamed (9718_CR49) 2018; 42 9718_CR21 9718_CR66 A Mohsenzadeh (9718_CR50) 2018; 39 XB Meng (9718_CR44) 2016; 28 9718_CR68 9718_CR69 9718_CR26 9718_CR27 XZ Gao (9718_CR24) 2015; 2015 H Hu (9718_CR30) 2017; 1 DS Kumar (9718_CR32) 2018; 118 9718_CR63 9718_CR64 S Mirjalili (9718_CR47) 2014; 69 9718_CR9 D Karaboga (9718_CR31) 2008; 8 DE Goldberg (9718_CR25) 1988; 3 KK Mishra (9718_CR48) 2010; 1 G Sun (9718_CR62) 2018; 6 XS Yang (9718_CR71) 2010; 2 9718_CR1 D Wu (9718_CR70) 2016; 4 9718_CR2 9718_CR3 9718_CR34 9718_CR4 9718_CR6 K Ahmed (9718_CR8) 2018 9718_CR7 9718_CR72 9718_CR73 9718_CR74 Y Li (9718_CR33) 2017; 30 9718_CR75 S Liang (9718_CR35) 2017; 11 R Poli (9718_CR55) 2007; 1 U Sultana (9718_CR61) 2016; 111 M Črepinšek (9718_CR16) 2013; 45 M Dorigo (9718_CR22) 2005; 344 W Shi (9718_CR59) 2018; 18 S Sivasakthi (9718_CR60) 2016; 2 S Torabi (9718_CR65) 2018; 74 M Shayokh (9718_CR58) 2017; 97 GG Wang (9718_CR67) 2016; 8 S Deb (9718_CR20) 2018; 11 XB Meng (9718_CR45) 2018; 1 9718_CR41 9718_CR42
References_xml	– reference: LiangSFengTSunGSidelobe-level suppression for linear and circular antenna arrays via the cuckoo search–chicken swarm optimisation algorithmIET Microw Antennas Propag201711220921810.1049/iet-map.2016.0083 – reference: MengXBGaoXZLiuYZhangHA novel bat algorithm with habitat selection and Doppler effect in echoes for optimizationExpert Syst Appl20154217–186350636410.1016/j.eswa.2015.04.026 – reference: KumarDSVeniSEnhanced energy steady clustering using convergence node based path optimization with hybrid Chicken Swarm algorithm in MANETInt J Pure Appl Math2018118767788 – reference: SivasakthiSMuralikrishnanNChicken Swarm Optimization for economic dispatch with disjoint prohibited zones considering network lossesJ Appl Sci Eng Methodol201622255259 – reference: Dhiman G, Kaur A (2017) Spotted hyena optimizer for solving engineering design problems. In: 2017 International conference on machine learning and data science (MLDS). IEEE, pp 114–119 – reference: Sutoyo E, Saedudin RR, Yanto ITR, Apriani A (2017) Application of adaptive neuro-fuzzy inference system and chicken swarm optimization for classifying river water quality. In: 2017 5th international conference on electrical, electronics and information engineering (ICEEIE). IEEE, pp 118–122 – reference: Wu D, Kong F, Gao W, Shen Y, Ji Z (2015) Improved Chicken Swarm Optimization. In: 2015 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER). IEEE, pp 681–686 – reference: Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249 – reference: Moldovan D, Chifu V, Pop C, Cioara T, Anghel I, Salomie I (2018) Chicken Swarm Optimization and deep learning for manufacturing processes. In: 2018 17th RoEduNet conference: networking in education and research (RoEduNet). IEEE, pp 1–6 – reference: PoliRKennedyJBlackwellTParticle swarm optimizationSwarm Intell200711335710.1007/s11721-007-0002-0 – reference: Basha SH, Tharwat A, Ahmed K, Hassanien AE (2018) A predictive model for seminal quality using neutrosophic rule-based classification system. In: International conference on advanced intelligent systems and informatics. Springer, Cham, pp 495–504 – reference: KarabogaDBasturkBOn the performance of artificial bee colony (ABC) algorithmAppl Soft Comput20088168769710.1016/j.asoc.2007.05.007 – reference: Zareiegovar G, Fesaghandis RR, Azad MJ (2012) Optimal DG location and sizing in distribution system to minimize losses, improve voltage stability, and voltage profile. In: Proceedings of 17th conference on electrical power distribution networks (EPDC), pp 1–6 – reference: SultanaUKhairuddinABMokhtarASZareenNSultanaBGrey wolf optimizer based placement and sizing of multiple distributed generation in the distribution systemEnergy201611152553610.1016/j.energy.2016.05.128 – reference: Hafez AI, Zawbaa HM, Emary E, Mahmoud HA, Hassanien AE (2015) An innovative approach for feature selection based on chicken swarm optimization. In: 2015 7th international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 19–24 – reference: YangXSFirefly algorithm, stochastic test functions and design optimisationInt J Bio-Inspir Comput201022788410.1504/IJBIC.2010.032124 – reference: Banerjee S, Chattopadhyay S (2015) Improved serially concatenated convolution turbo code (SCCTC) using chicken swarm optimization. In: Power, communication and information technology conference (PCITC), 2015 IEEE. IEEE, pp 268–273 – reference: HertzAKoblerDA framework for the description of evolutionary algorithmsEur J Oper Res2000126111217816040970.9012210.1016/S0377-2217(99)00435-X – reference: LiuDLiuCFuQLiTKhanMICuiSFaizMAProjection pursuit evaluation model of regional surface water environment based on improved Chicken Swarm Optimization algorithmWater Resour Manag201732118 – reference: QuCZhaoSAFuYHeWChicken swarm optimization based on elite opposition-based learningMath Probl Eng20172017203633344 – reference: Taie SA, Ghonaim W (2017) CSO-based algorithm with support vector machine for brain tumor’s disease diagnosis. In: 2017 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops). IEEE, pp 183–187 – reference: Meng XB, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: Chicken Swarm Optimization. In: International conference in swarm intelligence. Springer, Cham, pp 86–94 – reference: Han M, Liu S (2017) An improved binary chicken swarm optimization algorithm for solving 0–1 Knapsack problem. In: 2017 13th international conference on computational intelligence and security (CIS). IEEE, pp 207–210 – reference: ChenYLHePLZhangYHCombining penalty function with modified chicken swarm optimization for constrained optimizationAdv Intell Syst Res201512618991907 – reference: DebSTammiKKalitaKMahantaPImpact of electric vehicle charging station load on distribution networkEnergies201811117810.3390/en11010178 – reference: GoldbergDEHollandJHGenetic algorithms and machine learningMach Learn198832959910.1023/A:1022602019183 – reference: DasSSuganthanPNDifferential evolution: a survey of the state-of-the-artIEEE Trans Evol Comput201115143110.1109/TEVC.2010.2059031 – reference: MirjaliliSMirjaliliSMLewisAGrey wolf optimizerAdv Eng Softw201469466110.1016/j.advengsoft.2013.12.007 – reference: MohsenzadehAPazoukiSArdalanSHaghifamMROptimal placing and sizing of parking lots including different levels of charging stations in electric distribution networksInt J Ambient Energy201839774375010.1080/01430750.2017.1345010 – reference: Liang S, Feng T, Sun G, Zhang J, Zhang H (2016) Transmission power optimization for reducing sidelobe via bat-chicken swarm optimization in distributed collaborative beamforming. In: 2016 2nd IEEE international conference on computer and communications (ICCC). IEEE, pp 2164–2168 – reference: ShiWGuoYYanSYuYLuoPLiJOptimizing directional reader antennas deployment in UHF RFID localization system by using a MPCSO algorithmIEEE Sens J201818125035504810.1109/JSEN.2018.2832216 – reference: ZhangHZhangXGaoXZSongSSelf-organizing multiobjective optimization based on decomposition with neighborhood ensembleNeurocomputing20161731868188410.1016/j.neucom.2015.08.092 – reference: McGrathNBurmanODwyerCPhillipsCJDoes the anticipatory behaviour of chickens communicate reward quality?Appl Anim Behav Sci2016184809010.1016/j.applanim.2016.08.010 – reference: IrsalindaNThobirinAWijayantiDEChicken swarm as a multi step algorithm for global optimizationInt J Eng Sci Invent201761814 – reference: LiYWuYQuXChicken Swarm-based method for ascent trajectory optimization of hypersonic vehiclesJ Aerosp Eng20173050401704310.1061/(ASCE)AS.1943-5525.0000757 – reference: MengXBLiHXGaoXZAn adaptive reinforcement learning-based bat algorithm for structural design problemsInt J Bio Inspir Comput2018111(in press) – reference: WangGGDebSGaoXZCoelhoLDSA new metaheuristic optimisation algorithm motivated by elephant herding behaviourInt J Bio-Inspir Comput20168639440910.1504/IJBIC.2016.081335 – reference: ČrepinšekMLiuSHMernikMExploration and exploitation in evolutionary algorithms: a surveyACM Comput Surv (CSUR)2013453351293.6825110.1145/2480741.2480752 – reference: Ahmed K, Ewees AA, Hassanien AE (2017) Prediction and management system for forest fires based on hybrid flower pollination optimization algorithm and adaptive neuro-fuzzy inference system. In: 2017 Eighth international conference on intelligent computing and information systems (ICICIS). IEEE, pp 299–304 – reference: Mu Y, Zhang L, Chen X, Gao X (2016) Optimal trajectory planning for robotic manipulators using chicken swarm optimization. In: 2016 8th international conference on intelligent human–machine systems and cybernetics (IHMSC), vol 2. IEEE, pp 369–373 – reference: Abbas Z, Javaid N, Khan AJ, Rehman MHA, Sahi J, Saboor A (2018) Demand side energy management using hybrid chicken swarm and bacterial foraging optimization techniques. In: 2018 IEEE 32nd international conference on advanced information networking and applications (AINA), IEEE, pp 445–456 – reference: Ahmed K, Hassanien AE, Bhattacharyya S (2017) A novel chaotic chicken swarm optimization algorithm for feature selection. In: 2017 Third international conference on research in computational intelligence and communication networks (ICRCICN). IEEE, pp 259–264 – reference: Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214 – reference: ChengMYPrayogoDSymbiotic organisms search: a new metaheuristic optimization algorithmComput Struc20141399811210.1016/j.compstruc.2014.03.007 – reference: HuHLiJHuangJEconomic operation optimization of micro-grid based on Chicken Swarm Optimization algorithmHigh Volt Appar20171020 – reference: Pei Y, Hao J (2017) Non-dominated sorting and crowding distance based multi-objective chaotic evolution. In: International conference in swarm intelligence. Springer, Cham, pp 15–22 – reference: Poli R, Langdon WB (1998) On the search properties of different crossover operators in genetic programming. In: Genetic programming 1998: proceedings of third annual conference, University of Wisconsin, Madison. Morgan Kaufmann, pp 293–301 – reference: LogeshRSubramaniyaswamyVVijayakumarVGaoXZIndragandhiVA hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart cityFuture Gener Comput Syst20188365367310.1016/j.future.2017.08.060 – reference: DorigoMBlumCAnt colony optimization theory: a surveyTheoret Comput Sci20053442–324327821788551154.9062610.1016/j.tcs.2005.05.020 – reference: ShayokhMShinSYBio inspired distributed WSN localization based on Chicken Swarm OptimizationWireless Pers Commun20179745691570610.1007/s11277-017-4803-1 – reference: CaiXGaoXZXueYImproved bat algorithm with optimal forage strategy and random disturbance strategyInt J Bio-Inspir Comput20168420521410.1504/IJBIC.2016.078666 – reference: GaoXZGovindasamyVXuHWangXZengerKHarmony search method: theory and applicationsComput Intell Neurosci201520153910.1155/2015/258491 – reference: MishraKKHaritSA fast algorithm for finding the non dominated set in multi objective optimizationInt J Comput Appl20101253539 – reference: MohamedTMEnhancing The performance of the greedy algorithm using Chicken Swarm Optimization: an application to exam scheduling problemEgypt Comput Sci J201842113776448 – reference: AhmedKHassanienAEEzzatEHassanienAEGaberTAn efficient approach for community detection in complex social networks based on elephant swarm optimization algorithmHandbook of research on machine learning innovations and trends2017HersheyIGI Global1062107510.4018/978-1-5225-2229-4.ch047 – reference: SunGLiuYLiangSChenZWangAJuQZhangYA sidelobe and energy optimization array node selection algorithm for collaborative beamforming in wireless sensor networksIEEE Access201862515253010.1109/ACCESS.2017.2783969 – reference: MarinoLThinking chickens: a review of cognition, emotion, and behavior in the domestic chickenAnim Cogn201720212714710.1007/s10071-016-1064-4 – reference: Yi Z, Liu J, Wang S, Zeng X, Lu J (2016) PAPR reduction technology based on CSO algorithm in CO-OFDM system. In: 2016 15th international conference on optical communications and networks (ICOCN). IEEE, pp 1–3 – reference: WuDXuSKongFConvergence analysis and improvement of the chicken swarm optimization algorithmIEEE Access201649400941210.1109/ACCESS.2016.2604738 – reference: AhmedKBabersRDarwishAHassanienAEPandaMAbrahamAHassanienAESwarm-based analysis for community detection in complex networksBig data analytics a social network approach2018LondonTaylor and Francis18 – reference: MengXBLiHXYangHDEvolutionary design of spatiotemporal leaning model for thermal distribution in Lithium-ion batteriesIEEE Trans Industr Inf20181199 – reference: Ahmed K, Ewees AA, El Aziz MA, Hassanien AE, Gaber T, Tsai PW, Pan JS (2016) A hybrid krill-ANFIS model for wind speed forecasting. In: International conference on advanced intelligent systems and informatics. Springer, Cham, pp 365–372 – reference: Ahmed K, Hassanien AE, Ezzat E, Tsai PW (2016) An adaptive approach for community detection based on chicken swarm optimization algorithm. In: International conference on genetic and evolutionary computing. Springer, Cham, pp 281–288 – reference: TorabiSSafi-EsfahaniFA dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computingJ Supercomput20187462581262610.1007/s11227-018-2291-z – reference: MarinakisYDouniasGNature inspired intelligence in medicine: ant colony optimization for pap-smear diagnosisInt J Artif Intell Tools2008170227930110.1142/S0218213008003893 – reference: Meng XB, Li HX (2017) Dempster–Shafer based probabilistic fuzzy logic system for wind speed prediction. In: 2017 international conference on fuzzy theory and its applications (iFUZZY). IEEE, pp 1–5 – reference: Deb S, Kalita K, Gao XZ, Tammi K, Mahanta P (2017) Optimal placement of charging stations using CSO-TLBO algorithm. In: 2017 Third international conference on research in computational intelligence and communication networks (ICRCICN). IEEE, pp 84–89 – reference: MengXBGaoXZLuLLiuYZhangHA new bio-inspired optimisation algorithm: bird swarm algorithmJ Exp Theor Artif Intell201628467368710.1080/0952813X.2015.1042530 – reference: Ren W, Deng C, Zhang C, Mao Y (2017) Identification of fast-steering mirror based on chicken swarm optimization algorithm. In: IOP conference series: earth and environmental science, vol 69, no 1. IOP Publishing, p 012086 – reference: GandomiAHAlaviAHKrill herd: a new bio-inspired optimization algorithmCommun Nonlinear Sci Numer Simul201217124831484529602791266.6509210.1016/j.cnsns.2012.05.010 – reference: Awal AR, Dou Z, Al Shayokh M, Zahoor MI (2017) Implementation of chicken swarm optimization (CSO) with partial transmit sequences for the reduction of PAPR in OFDM system. In: 2017 IEEE 9th international conference on communication software and networks (ICCSN). IEEE, pp 468–472 – reference: Deb S, Kalita K, Gao XZ, Tammi K, Mahanta P (2018a) A pareto dominance based multi-objective Chicken Swarm Optimization and teaching learning based optimization algorithm for charging station placement problem. Int Trans Electr Energy Syst (to be communicated) – reference: ChenSYangRYangRYangLYangXXuCLiuWA parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimizationDiscrete Dyn Nat Soc2016201611 – reference: Ahmed K, Hassanien AE, Ezzat E, Bhattacharyya S (2018) Swarming behaviors of chicken for predicting posts on facebook branding pages. In: International conference on advanced machine learning technologies and applications. Springer, Cham, pp 52–61 – reference: HengJWangCZhaoXXiaoLResearch and application based on adaptive boosting strategy and modified CGFPA algorithm: a case study for wind speed forecastingSustainability20168323510.3390/su8030235 – reference: Wang Q, Zhu L (2017) Optimization of wireless sensor networks based on chicken swarm optimization algorithm. In: AIP conference proceedings, vol 1839, no 1. AIP Publishing, p 020197 – reference: Wang K, Li Z, Cheng H, Zhang K (2017) Mutation chicken swarm optimization based on nonlinear inertia weight. In: 2017 3rd IEEE international conference on computer and communications (ICCC). IEEE, pp 2206–2211 – ident: 9718_CR68 doi: 10.1109/CompComm.2017.8322928 – ident: 9718_CR19 – ident: 9718_CR11 – volume: 45 start-page: 35 issue: 3 year: 2013 ident: 9718_CR16 publication-title: ACM Comput Surv (CSUR) doi: 10.1145/2480741.2480752 – ident: 9718_CR57 doi: 10.1088/1755-1315/69/1/012086 – ident: 9718_CR73 – ident: 9718_CR52 doi: 10.1109/IHMSC.2016.107 – start-page: 18 volume-title: Big data analytics a social network approach year: 2018 ident: 9718_CR8 – ident: 9718_CR54 – ident: 9718_CR27 doi: 10.1109/CIS.2017.00052 – volume: 2015 start-page: 39 year: 2015 ident: 9718_CR24 publication-title: Comput Intell Neurosci doi: 10.1155/2015/258491 – volume: 1 start-page: 020 year: 2017 ident: 9718_CR30 publication-title: High Volt Appar – volume: 139 start-page: 98 year: 2014 ident: 9718_CR15 publication-title: Comput Struc doi: 10.1016/j.compstruc.2014.03.007 – ident: 9718_CR51 doi: 10.1109/ROEDUNET.2018.8514152 – volume: 28 start-page: 673 issue: 4 year: 2016 ident: 9718_CR44 publication-title: J Exp Theor Artif Intell doi: 10.1080/0952813X.2015.1042530 – volume: 2 start-page: 78 issue: 2 year: 2010 ident: 9718_CR71 publication-title: Int J Bio-Inspir Comput doi: 10.1504/IJBIC.2010.032124 – ident: 9718_CR4 – volume: 30 start-page: 04017043 issue: 5 year: 2017 ident: 9718_CR33 publication-title: J Aerosp Eng doi: 10.1061/(ASCE)AS.1943-5525.0000757 – volume: 42 start-page: 1 issue: 1 year: 2018 ident: 9718_CR49 publication-title: Egypt Comput Sci J – ident: 9718_CR63 doi: 10.1109/ICEEIE.2017.8328774 – ident: 9718_CR42 doi: 10.1007/978-3-319-11857-4_10 – volume: 184 start-page: 80 year: 2016 ident: 9718_CR40 publication-title: Appl Anim Behav Sci doi: 10.1016/j.applanim.2016.08.010 – ident: 9718_CR41 – ident: 9718_CR10 doi: 10.1109/PCITC.2015.7438173 – ident: 9718_CR66 doi: 10.1063/1.4982562 – volume: 8 start-page: 205 issue: 4 year: 2016 ident: 9718_CR12 publication-title: Int J Bio-Inspir Comput doi: 10.1504/IJBIC.2016.078666 – volume: 2 start-page: 255 issue: 2 year: 2016 ident: 9718_CR60 publication-title: J Appl Sci Eng Methodol – volume: 344 start-page: 243 issue: 2–3 year: 2005 ident: 9718_CR22 publication-title: Theoret Comput Sci doi: 10.1016/j.tcs.2005.05.020 – volume: 126 start-page: 1899 year: 2015 ident: 9718_CR13 publication-title: Adv Intell Syst Res – volume: 118 start-page: 767 year: 2018 ident: 9718_CR32 publication-title: Int J Pure Appl Math – volume: 1 start-page: 33 issue: 1 year: 2007 ident: 9718_CR55 publication-title: Swarm Intell doi: 10.1007/s11721-007-0002-0 – ident: 9718_CR26 doi: 10.1109/SOCPAR.2015.7492775 – volume: 1 start-page: 99 issue: 1 year: 2018 ident: 9718_CR45 publication-title: IEEE Trans Industr Inf – volume: 11 start-page: 178 issue: 1 year: 2018 ident: 9718_CR20 publication-title: Energies doi: 10.3390/en11010178 – volume: 11 start-page: 209 issue: 2 year: 2017 ident: 9718_CR35 publication-title: IET Microw Antennas Propag doi: 10.1049/iet-map.2016.0083 – volume: 2017 start-page: 20 year: 2017 ident: 9718_CR56 publication-title: Math Probl Eng – volume: 69 start-page: 46 year: 2014 ident: 9718_CR47 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2013.12.007 – volume: 18 start-page: 5035 issue: 12 year: 2018 ident: 9718_CR59 publication-title: IEEE Sens J doi: 10.1109/JSEN.2018.2832216 – volume: 83 start-page: 653 year: 2018 ident: 9718_CR37 publication-title: Future Gener Comput Syst doi: 10.1016/j.future.2017.08.060 – ident: 9718_CR18 doi: 10.1109/ICRCICN.2017.8234486 – ident: 9718_CR69 doi: 10.1109/CYBER.2015.7288023 – volume: 111 start-page: 525 year: 2016 ident: 9718_CR61 publication-title: Energy doi: 10.1016/j.energy.2016.05.128 – volume: 8 start-page: 394 issue: 6 year: 2016 ident: 9718_CR67 publication-title: Int J Bio-Inspir Comput doi: 10.1504/IJBIC.2016.081335 – volume: 17 start-page: 4831 issue: 12 year: 2012 ident: 9718_CR23 publication-title: Commun Nonlinear Sci Numer Simul doi: 10.1016/j.cnsns.2012.05.010 – start-page: 1062 volume-title: Handbook 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start-page: 11 year: 2016 ident: 9718_CR14 publication-title: Discrete Dyn Nat Soc – volume: 8 start-page: 235 issue: 3 year: 2016 ident: 9718_CR28 publication-title: Sustainability doi: 10.3390/su8030235 – volume: 42 start-page: 6350 issue: 17–18 year: 2015 ident: 9718_CR43 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.04.026 – ident: 9718_CR1 doi: 10.1109/AINA.2018.00073 – volume: 6 start-page: 8 issue: 1 year: 2017 ident: 9718_CR77 publication-title: Int J Eng Sci Invent – ident: 9718_CR34 doi: 10.1109/CompComm.2016.7925083 – volume: 173 start-page: 1868 year: 2016 ident: 9718_CR76 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.092 – ident: 9718_CR7 doi: 10.1007/978-3-319-74690-6_6 – volume: 97 start-page: 5691 issue: 4 year: 2017 ident: 9718_CR58 publication-title: Wireless Pers Commun doi: 10.1007/s11277-017-4803-1 – ident: 9718_CR74 – ident: 9718_CR53 doi: 10.1007/978-3-319-61833-3_2 – ident: 9718_CR3
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Snippet	Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in...
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SubjectTerms	Algorithms Artificial Intelligence Biology Birds Computer Science Distributed generation Feature selection Food Foraging behavior Genetic algorithms Heuristic Heuristic methods Mathematical optimization Mechanical engineering Meta-analysis Optimization Optimization algorithms Poultry Route planning Social networks Unit commitment Variants Vehicle routing
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Title	Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)
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