Abaza,A., El Sehiemy, R.A., El-Fergany,A., & Bayoumi,A. S. A. (2022). Optimal parameter estimation of solid oxide fuel cells model using bald eagle search optimizer. International Journal of Energy Research, 46(10),13657-13669. 

Abdechiri, M., Meybodi, M. R., & Bahrami, H. (2013). Gases Brownian motion optimization:analgorithmfor optimization (GBMO). Applied Soft Computing, 13(5),2932-2946. 

Abdullah, S., & Jaddi, N. S. (2010). Great deluge algorithm for rough set attribute reductionDatabase theory and application, bio-science and bio-technology (pp. 189-197):Springer. 

Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: a physics-inspired metaheuristic optimizationalgorithm. Swarm and Evolutionary Computation, 26, 8-22. 

Agushaka,J.O.,Ezugwu,A.E.,&Abualigah,L.(2022).Dwarf mongooseoptimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570. 

Ahmad, F., Iqbal,A.,Ashraf, I., Marzband, M., & Khan, I. (2022). Optimal Siting and Sizing Approach of Plug-in Electric Vehicle Fast Charging Station using a Novel Meta-heuristic Algorithm. Paper presented at the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). 

Ahmadi-Javid, A. (2011). Anarchic society optimization: a human-inspired method. Paper presentedatthe2011IEEEcongressof evolutionarycomputation(CEC). 

Ahrari, A., & Atai, A. A. (2010). Grenade explosion method—a novel tool for optimization of multimodal functions. Applied Soft Computing, 10(4), 1132­1140. 

Al-Khayyal,F.,& Pardalos,P. (1991).Newcomputer methodsfor globaloptimization 

(H. Ratschekand J. Rokne). SIAM Review, 33(4),684-686. Al-Shaikhi,A., Nuha, H., Mohandes,M., Rehman, S., &Adrian, M. (2022).Vertical wind speed extrapolation model using long short-term memory and particle swarmoptimization. Energy Science & Engineering. Alatas, B. (2011). ACROA: artificial chemical reaction optimization algorithm for globaloptimization. Expert Systems with Applications, 38(10),13170-13180. 

Alauddin,M.(2016).Mosquito flying optimization (MFO). Paper presentedatthe2016 international conference on electrical, electronics, and optimization techniques (ICEEOT). 

Algarni,A.D.,Alturki,N.,Soliman,N.F.,Abdel-Khalek,S.,&Mousa,A.A.A.(2022). An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images. Canadian Journal of Remote Sensing, 1-12. 

Alhasnawi, B. N., Jasim, B. H., Siano, P., Alhelou, H. H., & Al-Hinai, A. (2022). A NovelSolutionfor Day-AheadSchedulingProblemsUsingtheIoT-BasedBald EagleSearchOptimizationAlgorithm. Inventions, 7(3),48. 

Almonacid, B., & Soto, R. (2019). Andean Condor Algorithm for cell formation problems. Natural Computing, 18(2),351-381. 

Alsaidan,I., Shaheen,M.A.,Hasanien,H. M.,Alaraj,M.,&Alnafisah,A. S. (2022).A PEMFC model optimization using the enhanced bald eagle algorithm. Ain Shams Engineering Journal, 13(6),101749. 

AlSattar, H., Zaidan,A., Zaidan, B.,Abu Bakar, M., Mohammed, R.,Albahri, O., . . . Albahri,A. (2020). MOGSABAT: ametaheuristichybridalgorithmfor solving multi-objective optimisation problems. Neural computing and applications, 32(8),3101-3115. 

Alsubai,S., Hamdi,M.,Abdel-Khalek,S.,Alqahtani,A., Binbusayyis,A.,& Mansour, R.F.(2022).Baldeaglesearchoptimizationwithdeeptransfer learningenabled age-invariantfacerecognitionmodel. Image and Vision Computing,104545. 

Angeline, P. J. (1994). Genetic programming: On the programming of computers by means of natural selection: John R. Koza, A Bradford Book, MIT Press, CambridgeMA,1992,ISBN0-262-11170-5,xiv+819pp.,US$55.00:Elsevier. 

Anjos,M.F.,&Vieira,M.V.(2017).Mathematicaloptimizationapproachesfor facility layout problems: The state-of-the-art and future research directions. European Journal of Operational Research, 261(1),1-16. 

Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for globaloptimization. Soft Computing, 23(3),715-734. 

Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 169, 1-12. 

Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Paper presentedatthe2007IEEEcongressonevolutionary computation. 

AYS, L., & VOK, L. (2010). Chemical-Reaction-Inspired Metaheuristic for Optimization.IEEE transactions on evolutionary computation, 14(3),381–399. 

Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel 

metaheuristicalgorithm. Artificial Intelligence Review, 1-77. 

Bagirathan, K., & Palanisamy,A. (2022). Opportunistic routing protocol based EPO– BESinMANETfor optimalpathselection. Wireless Personal Communications, 123(1),473-494. 

Bandyopadhyay, S., Saha, S., Maulik, U., & Deb, K. (2008).A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE transactions on evolutionary computation, 12(3),269-283. 

Beheshti, Z., & Shamsuddin, S. M. H. (2013). A review of population-based meta­heuristicalgorithms. Int. J. Adv. Soft Comput. Appl, 5(1),1-35. 

Beni,G.,&Wang,J.(1993).Swarmintelligenceincellular roboticsystems Robots and biological systems: towards a new bionics? (pp. 703-712):Springer. 

Bent,R.,&Van Hentenryck,P. (2004).Atwo-stagehybrid localsearchfor thevehicle routingproblemwith timewindows. Transportation Science, 38(4),515-530. 

Bharanidharan, N., Sannasi Chakravarthy, S., & Rajaguru, H. (2022). Improved Bald Eagle Search Optimization for Enhancing the Performance of Supervised Classifiers in Dementia Diagnosis. Paper presented at the Kuala Lumpur InternationalConferenceonBiomedicalEngineering. 

Birattari, M., Paquete, L., Stützle, T., & Varrentrapp, K. (2001). Classification of metaheuristicsanddesignof experimentsfor theanalysisof components.Teknik Rapor, AIDA-01-05. 

Birge, B. (2003). PSOt-a particle swarm optimization toolbox for use with Matlab. Paper presented at the Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706). 

Bitam, S., Zeadally, S., & Mellouk, A. (2018). Fog computing job scheduling optimizationbasedonbeesswarm. Enterprise Information Systems, 12(4),373­397. 

Blum, C., & Roli,A. (2003). Metaheuristics in combinatorial optimization: Overview andconceptualcomparison. ACM computing surveys (CSUR), 35(3),268-308. 

Bogar,E.,&Beyhan,S.(2020).AdolescentIdentitySearchAlgorithm(AISA):Anovel metaheuristic approach for solving optimization problems. Applied Soft Computing, 95,106503. 

Boppana, V., & Sandhya, P. (2021). Web crawling based context aware recommender system using optimized deep recurrent neural network. Journal of Big Data, 8(1),1-24. 

Brabazon, A., Cui, W., & O’Neill, M. (2015). The raven roosting optimisation algorithm. Soft Computing, 20(2),525-545. 

Bujok,P.,Tvrdík,J.,&Poláková,R.(2019).Comparisonof nature-inspiredpopulation­based algorithms on continuous optimisation problems. Swarm and Evolutionary Computation, 50,100490. 

Cai, S., Li, X., Li, S., Luo, X., & Tu, Z. (2022). Flexible load regulation method for a residentialenergysupplysystembasedonprotonexchangemembranefuelcell. Energy Conversion and Management, 258,115527. 

Cao, J., Zhang, J., Liu, M., Yin, S., & An, Y. (2022). Green Logistics of Vehicle Dispatchunder SmartIoT. Sensors and Materials, 34(8),3317-3338. 

Caramia, M., & Dell’Olmo, P. (2020). Multi-objective optimization Multi-objective management in freight logistics (pp. 21-51):Springer. 

Cavazzuti, M. (2013). Deterministic optimization Optimization methods (pp. 77-102): Springer. 

Cengiz, Y., & Tokat, H. (2008). Linear antenna array design with use of genetic, memeticandtabusearchoptimizationalgorithms.Progress In Electromagnetics Research C, 1,63-72. 

ÇETINKAYA, M. B., & TASKIRAN, K. Meta-SezgiselAlgoritmalara Dayali Retinal Damar Bölütleme. Journal of Materials and Mechatronics: A, 3(1),79-90. 

Chakraborty, S., Sharma, S., Saha,A. K., & Saha,A. (2022).Anovel improved whale optimization algorithm to solve numerical optimization and real-world applications. Artificial Intelligence Review, 1-112. 

Che, J., Tong, X., &Yu, L. (2022).Adynamic bidirectional heuristic trust path search algorithm. CAAI Transactions on Intelligence Technology. 

Chen,D.,&Cheng,P. (2022).Perceptualevaluationfor Zhangpupaper-cutpatternsby using improved GWO-BP neural network. International Journal of Nonlinear Sciences and Numerical Simulation. 

Cheng,L.,Wu,X.-h.,&Wang,Y. (2018).Artificialflora(AF) optimization algorithm. Applied Sciences, 8(3),329. 

Cheraghalipour, A., Hajiaghaei-Keshteli, M., & Paydar, M. M. (2018). Tree Growth Algorithm (TGA): A novel approach for solving optimization problems. Engineering Applications of Artificial Intelligence, 72,393-414. 

Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimizationproblems.Applied Mathematics and Computation, 219(15),8121­8144. 

Comellas,F., & Martinez-Navarro,J. (2009).Bumblebees:amultiagentcombinatorial optimization algorithm inspired by social insect behaviour Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation (pp.811­814). 

Corne, D., & Lones, M. A. (2018). Evolutionary algorithms Handbook of Heuristics 

(pp. 409-430):Springer. 

Cortinhal, M. J., Mourão, M. C., & Nunes,A. C. (2016). Local search heuristics for sectoringrouting in ahousehold wastecollectioncontext. European Journal of Operational Research, 255(1),68-79. 

De Castro, L. N., & Von Zuben, F. J. (2000). The clonal selection algorithm with engineering applications. Paper presentedattheProceedingsof GECCO. 

Deb, K. (2014). Multi-objective optimization Search methodologies (pp. 403-449): Springer. 

DelAcebo, E., & de-la Rosa, J. L. (2008). Introducing bar systems: a class of swarm intelligence optimization algorithms. Paper presented at the AISB 2008 ConventionCommunication,Interaction andSocialIntelligence. 

Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J.-C., & Harley, 

R. G. (2008). Particle swarm optimization: basic concepts, variants and applicationsinpower systems.IEEE transactions on evolutionary computation, 12(2),171-195. 

Derrac, J., García, S., Molina, D., & Herrera, F. (2011).Apractical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1),3-18. 

Dian,S.,Zhong,J.,Guo,B.,Liu,J.,&Guo,R.(2022).Asmoothpathplanningmethod for mobile robot using a BES-incorporated modified QPSO algorithm. Expert Systems with Applications, 208,118256. 

Diwekar, U. M. (2020). Optimization under uncertainty Introduction to Applied Optimization (pp. 151-215):Springer. 

Dong,Y.,Li, J., Liu,Z.,Niu,X.,&Wang,J. (2022).Ensemblewindspeed forecasting systembasedonoptimalmodeladaptiveselectionstrategy:CasestudyinChina. Sustainable Energy Technologies and Assessments, 53,102535. 

Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4),28-39. 

Drigo, M. (1996). TheAnt System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1),1-13. 

Duman, E., Uysal, M., & Alkaya, A. F. (2012). Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Information sciences, 217,65-77. 

Duman, S., Kahraman, H. T., Sonmez,Y., Guvenc, U., Kati, M., &Aras, S. (2022).A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Engineering Applications of Artificial Intelligence, 111,104763. 

Eberhart,R.,&Kennedy,J.(1995). A new optimizer using particle swarm theory. Paper presented at the MHS'95. Proceedingsof the sixth international symposiumon micromachineand humanscience. 

Eita, M., & Fahmy, M. (2014). Group counseling optimization. Applied Soft Computing, 22,585-604. 

Ellabib,I.,Calamai,P.,&Basir,O. (2007).Exchangestrategiesfor multipleantcolony system. Information sciences, 177(5),1248-1264. 

Elshaer, R., & Awad, H. (2020).A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial 

Engineering, 140,106242. 

Emiliano,I.(2015).HeuristicReasoning:StudiesinAppliedPhilosophy,Epistemology 

andRationalEthics:Switzerland:Springer InternationalPublishing. 

Eusuff,M.,Lansey,K.,&Pasha,F.(2006).Shuffledfrog-leapingalgorithm:amemetic meta-heuristic for discrete optimization. Engineering optimization, 38(2), 129­154. 

Ezugwu,A.E.,Shukla,A. K.,Nath,R.,Akinyelu,A.A.,Agushaka,J. O.,Chiroma,H., & Muhuri, P. K. (2021). Metaheuristics: a comprehensive overview and classificationalongwithbibliometricanalysis. Artificial Intelligence Review,1­80. 

Fadakar, E., & Ebrahimi, M. (2016). A new metaheuristic football game inspired algorithm. Paper presented at the 2016 1st conference on swarm intelligence andevolutionary computation(CSIEC). 

Fard, A. M. F., & Hajiaghaei-Keshteli, M. (2017). Social Engineering Optimization (SEO);ANewSingle-Solution Meta-heuristicInspiredbySocialEngineering. 

Fathy, A., Ferahtia, S., Rezk, H., Yousri, D., Abdelkareem, M. A., & Olabi, A. G. (2022). Robust parameter estimation approach of Lithium-ion batteries employing bald eagle search algorithm. International Journal of Energy Research. 

Fathy,A.,Rezk,H.,Yousri,D.,Kandil,T.,&Abo-Khalil,A. G. (2022).Real-timebald eagle search approach for tracking the maximum generated power of wind energyconversion system. Energy, 249,123661. 

Ferahtia, S., Rezk, H., Djeroui,A., Houari,A., Motahhir, S., & Zeghlache, S. (2022). Modifiedbaldeaglesearchalgorithmfor lithium-ion batterymodelparameters extraction. ISA transactions. 

Fiacco, A. V., & McCormick, G. P. (1990). Nonlinear programming: sequential unconstrained minimization techniques:SIAM. 

Francisco, M., Revollar, S., Vega, P., & Lamanna, R. (2005).Acomparative study of deterministic and stochastic optimization methods for integrated design of processes. IFAC Proceedings Volumes, 38(1),335-340. 

Gambardella, L. M.,Taillard, É., &Agazzi, G. (1999). Macs-vrptw: A multiple colony system for vehicle routing problems with time windows. Paper presented at the Newideasinoptimization. 

Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: a new bio-inspired optimization algorithm. Communications in nonlinear science and numerical simulation, 17(12),4831-4845. 

Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2011). Mixed variable structural optimizationusingfireflyalgorithm. Computers & Structures, 89(23-24),2325­2336. 

Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1),17-35. 

Gao-Wei, Y., & Zhanju, H. (2012). A novel atmosphere clouds model optimization algorithm. Paper presented at the 2012 international conferenceon computing, measurement,controlandsensor network. 

Ghaemi, M., & Feizi-Derakhshi, M.-R. (2014). Forest optimization algorithm. Expert Systems with Applications, 41(15),6676-6687. 

Ghasemi,M.,Akbari,M.-A.,Jun,C.,Bateni,S. M.,Zare, M.,Zahedi,A., ...Chau,K.­

W. (2022). Circulatory System Based Optimization (CSBO): An expert 

multilevel biologically inspired meta-heuristic algorithm. Engineering Applications of Computational Fluid Mechanics, 16(1),1483-1525. 

Ghorbani, N., & Babaei, E. (2014). Exchange market algorithm. Applied Soft Computing, 19,177-187. 

Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & operations research, 13(5),533-549. 

Gorissen, B. L., Yanikoglu, I., & den Hertog, D. (2015). A practical guide to robust optimization. Omega, 53,124-137. 

Ha, M. C., Vu, P. L., Nguyen, H. D., Hoang, T. P., Dang, D. D., Dinh, T. B. H., . . . Bre.can, P. (2022). Machine Learning and Remote Sensing Application for Extreme Climate Evaluation: Example of Flood Susceptibility in the Hue Province,CentralVietnamRegion. Water, 14(10),1617. 

Häckel, S., & Dippold, P. (2009). The bee colony-inspired algorithm (BCiA) a two-stage approach for solving the vehicle routing problem with time windows. Paper presented at the Proceedings of the 11th Annual conference on Genetic andevolutionary computation. 

Hansen,A. J. (1986). Fighting behavior in bald eagles: atest of gametheory. Ecology, 67(3),787-797. 

Hartke, B. (2011). Global optimization. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(6),879-887. 

Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222,175-184. 

He, S., Wu, Q., & Saunders, J. (2006). A novel group search optimizer inspired by animal behavioural ecology. Paper presented at the 2006 IEEE international 

conferenceonevolutionarycomputation. 

He, S., Wu, Q. H., & Saunders, J. (2009). Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE transactions on evolutionary computation, 13(5),973-990. 

Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97,849-872. 

Holland,J. H. (1992).Geneticalgorithms. Scientific american, 267(1),66-73. 

Houck, C. R., Joines, J., & Kay, M. G. (1995). A genetic algorithm for function optimization:aMatlabimplementation. Ncsu-ie tr, 95(09),1-10. 

Huang, G. (2016). Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm. Swarm and Evolutionary Computation, 27,31-67. 

Huang, Y., Jiang, H., Wang, W., & Sun, D. Prediction Model of Soil Electrical Conductivity Based on ELM Optimized by Bald Eagle Search Algorithm. FACULTY OF ELECTRICAL ENGINEERING UNIVERSITY OF BANJA LUKA,50. 

Huang,Y.,Zhang,J.,Wei,W.,Qin,T.,Fan,Y.,Luo,X.,&Yang,J. (2022).Researchon Coverage Optimization in a WSN Based on an Improved COOT Bird Algorithm. Sensors, 22(9),3383. 

Hussain,K., MohdSalleh, M. N., Cheng,S., & Shi,Y. (2019).Metaheuristicresearch: acomprehensivesurvey. Artificial Intelligence Review, 52(4),2191-2233. 

Idros, N., Othman, W., Wahab,A., Noor, N., &Alhady, S. (2021). Simulating Solitary Foraging Behaviour of Chimpanzee in Hunting Red Colobus Monkeys Using Agent-Based Modelling Approach. Paper presented at the Symposium on 

IntelligentManufacturing andMechatronics. 

Inkollu,S. R.,Anjaneyulu,G. P.,NC, K., & CH, N. K.AnApplicationof Hunter-Prey Optimizationfor MaximizingPhotovoltaicHostingCapacityAlongwithMulti­ObjectiveOptimization in RadialDistribution Network. 

Irizarry, R. (2005). A generalized framework for solving dynamic optimization problems using the artificial chemical process paradigm: Applications to particulate processes and discrete dynamic systems. Chemical Engineering Science, 60,5663-5681. 

Jaddi, N. S., & Abdullah, S. (2013). Nonlinear great deluge algorithm for rough set attributereduction. Journal of Information Science and Engineering, 29(1),49­62. 

Jaddi, N. S., & Abdullah, S. (2020). Global search in single-solution-based metaheuristics. Data Technologies and Applications. 

Jaderyan,M.,& Khotanlou,H. (2016).Virulenceoptimizationalgorithm. Applied Soft Computing, 43,596-618. 

Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization:Squirrelsearchalgorithm. Swarm and Evolutionary Computation, 44,148-175. 

Jeyakumar,V.,&Rubinov,A.M.(2006).Continuous Optimization: Current Trends and Modern Applications (Vol. 99):Springer Science&BusinessMedia. 

Jia, N., Cheng, Y., Liu, Y., & Tian, Y. (2022). Intelligent Fault Diagnosis of Rotating Machines Based on WaveletTime-Frequency Diagram and OptimizedStacked DenoisingAuto-Encoder. IEEE Sensors Journal, 22(17),17139-17150. 

Jin, X., & Reynolds, R. G. (1999). Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm 

approach. Paper presented at the Proceedings of the 1999 congress on evolutionarycomputation-CEC99(Cat. No. 99TH8406). 

Joines,J.A., &Houck,C. R. (1994). On the Use of Non-Stationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems with GA's. Paper presentedattheInternationalConferenceon Evolutionary Computation. 

Kaboli,S. H.A., Selvaraj,J.,& Rahim,N. (2017).Rain-fall optimizationalgorithm:A population based algorithm for solving constrained optimization problems. Journal of Computational Science, 19,31-42. 

Kaisar, A., Giasin, K., Pimenov, D., & Wojciechowski, S. (2021). Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle SearchAlgorithms. Materials 2021, 14, 7479: s Note: MDPI stays neu­tralwithregardtojurisdictionalclaimsin …. 

Kapileswar, N., & Phani Kumar, P. (2022). Energy efficient routing in IOT based UWSN using bald eagle search algorithm. Transactions on Emerging Telecommunications Technologies, 33(1),e4399. 

Karaboga, D., & Basturk, B. (2007).Apowerful and efficient algorithm for numerical functionoptimization:artificialbeecolony(ABC) algorithm. Journal of global optimization, 39(3),459-471. 

Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: thermalexchangeoptimization. Advances in engineering software, 110,69-84. 

Kaveh,A.,& Farhoudi,N. (2013).Anewoptimizationmethod:Dolphinecholocation. Advances in engineering software, 59,53-70. 

Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war 

optimization. Iran University of Science & Technology, 6(4),469-492. 

Kazarlis, S., & Petridis, V. (1998). Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms. Paper presented at theInternationalconferenceonparallelproblemsolving fromnature. 

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at theProceedingsof ICNN'95-internationalconferenceonneuralnetworks. 

Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598),671-680. 

Korte,B.H.,Vygen,J.,Korte,B.,&Vygen,J.(2011). Combinatorial optimization (Vol. 1):Springer. 

Koza, J. R. (1994). Genetic programming as a means for programming computers by naturalselection. Statistics and computing, 4(2),87-112. 

Kumar,A.,Misra,R.K.,&Singh,D.(2015).Butterflyoptimizer 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI) (pp. 1-6). 

Lasserre, J. B. (2001). Global optimization with polynomials and the problem of moments. SIAM Journal on optimization, 11(3),796-817. 

Li, C., Chen, G., Liang, G., Luo, F., Zhao, J., & Dong, Z. Y. (2022). Integrated optimizationalgorithm:Ametaheuristicapproachfor complicatedoptimization. Information sciences, 586,424-449. 

Li,G.,Tang,Y.,&Yang,H. (2022).Anewhybridpredictionmodelof airqualityindex based on secondary decomposition and improved kernel extreme learning machine. Chemosphere, 305,135348. 

Li, M., Xu, G., Lai, Q., & Chen, J. (2022). A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm. Mathematics and Computers in Simulation, 193,71-99. 

Li, X. (2003).A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China, 27. 

Li, Y., Yuan, Q., Han, M., & Cui, R. (2022). Hybrid Multi-Strategy Improved Wild HorseOptimizer. Advanced Intelligent Systems,2200097. 

Li, Z., Lin, X., Zhang, Q., & Liu, H. (2020). Evolution strategies for continuous optimization: A survey of the state-of-the-art. Swarm and Evolutionary Computation, 56,100694. 

Liang,J.J.,Qin,A. K.,Suganthan,P.N.,&Baskar,S. (2006).Comprehensivelearning particleswarmoptimizer for globaloptimizationof multimodalfunctions. IEEE transactions on evolutionary computation, 10(3),281-295. 

Liang, J. J., Qu, B.Y., & Suganthan, P. N. (2013). Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numericaloptimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635,490. 

Lin, M.-H., Tsai, J.-F., & Yu, C.-S. (2012). A review of deterministic optimization methods in engineering and management. Mathematical Problems in Engineering, 2012. 

Liu,W.,Zhang,J.,Wei,W.,Qin,T.,Fan,Y.,Long,F.,&Yang,J.(2022).AHybridBald Eagle SearchAlgorithm for Time Difference of Arrival Localization. Applied Sciences, 12(10),5221. 

Liu, Y., & Passino, K. (2002). Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. Journal of optimization theory and applications, 115(3),603-628. 

Londhe, D., & Kumari, A. Multilingual Sentiment Analysis Using the Social Eagle­BasedBidirectionalLongShort-TermMemory. 

Long, W., Jiao, J., Liang, X., Xu, M., Wu, T., Tang, M., & Cai, S. (2022).Avelocity­guidedHarrishawksoptimizer for functionoptimization andfault diagnosisof windturbine. Artificial Intelligence Review, 1-43. 

Lu, F., Yan, T., Bi, H., Feng, M., Wang, S., & Huang, M. (2022). A bilevel whale optimization algorithm for risk management scheduling of information technology projects considering outsourcing. Knowledge-based systems, 235, 107600. 

Lynn,N.,& Suganthan,P. N. (2017).Ensembleparticleswarmoptimizer. Applied Soft Computing, 55,533-548. 

Ma, H., Shen, S., Yu, M., Yang, Z., Fei, M., & Zhou, H. (2019). Multi-population techniquesinnatureinspiredoptimizationalgorithms:Acomprehensivesurvey. Swarm and Evolutionary Computation, 44,365-387. 

Maged, N. A., Hasanien, H. M., Ebrahim, E. A., Tostado-Véliz, M., & Jurado, F. (2022). Real-time implementation and evaluation of gorilla troops optimization-based control strategy for autonomous microgrids. IET Renewable Power Generation. 

Malakooti, B., Kim, H., & Sheikh, S. (2012). Bat intelligence search with application to multi-objective multiprocessor scheduling optimization. The International Journal of Advanced Manufacturing Technology, 60(9),1071-1086. 

Man, K.-F., Tang, K.-S., & Kwong, S. (1996). Genetic algorithms: concepts and applications [in engineering design]. IEEE transactions on Industrial Electronics, 43(5),519-534. 

Mathur,M., Karale, S. B., Priye, S., Jayaraman,V.,&Kulkarni, B.(2000).Antcolony approach to continuous function optimization. Industrial & engineering chemistry research, 39(10),3814-3822. 

McDermott, J. (2020). When and why metaheuristics researcherscan ignore “No Free Lunch” theorems. SN Computer Science, 1(1),1-18. 

Melvix, J. L. (2014). Greedy politics optimization: Metaheuristic inspired by political strategies adopted during state assembly elections. Paper presentedatthe2014 IEEE internationaladvancecomputing conference(IACC). 

Meng, X.-B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm: Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4),673-687. 

Meng,X.,Liu,Y.,Gao,X.,&Zhang,H. (2014). A new bio-inspired algorithm: chicken swarm optimization. Paper presented at the International conference in swarm intelligence. 

Merrikh-Bayat, F. (2014). A numerical optimization algorithm inspired by the strawberryplant. arXiv preprint arXiv:1407.7399. 

Merrikh-Bayat, F. (2015). The runner-root algorithm: a metaheuristic for solving unimodalandmultimodaloptimization problemsinspired byrunnersandroots of plantsinnature. Applied Soft Computing, 33,292-303. 

Mirjalili, S. (2015a).Theantlionoptimizer. Advances in engineering software, 83,80­98. 

Mirjalili, S. (2015b). Moth-flame optimization algorithm: A novel nature-inspired heuristicparadigm. Knowledge-based systems, 89,228-249. 

Mirjalili, S. (2016).Dragonflyalgorithm:anewmeta-heuristicoptimization technique for solving single-objective, discrete, and multi-objective problems. Neural computing and applications, 27(4),1053-1073. 

Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95,51-67. 

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69,46-61. 

Moein, S., & Logeswaran, R. (2014). KGMO: a swarm optimization algorithm based on thekineticenergyof gasmolecules. Information sciences, 275,127-144. 

Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64,161-185. 

Molina, D., Poyatos, J., Ser, J. D., García, S., Hussain, A., & Herrera, F. (2020). Comprehensivetaxonomiesof nature-andbio-inspiredoptimization:Inspiration versus algorithmic behavior, critical analysis recommendations. Cognitive Computation, 12(5),897-939. 

Monga, P., Sharma, M., & Sharma, S. K. (2021).A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend. Journal of King Saud University-Computer and Information Sciences. 

Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. 

Mosa,M.A.,&Ali,A.(2021).Energymanagementsystemof lowvoltagedcmicrogrid usingmixed-integer nonlinear programingandaglobaloptimizationtechnique. Electric Power Systems Research, 192,106971. 

Mtibaa,A.,Ouni,B.,&Abid,M. (2007).Anefficientlistschedulingalgorithmfor time placementproblem. Computers & Electrical Engineering, 33(4),285-298. 

Muñoz Zavala, A. E., Aguirre, A. H., & Villa Diharce, E. R. (2005). Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). Paper presentedattheProceedingsof the7thannualconferenceonGeneticand evolutionarycomputation. 

Nassef, A. M., Fathy, A., Rezk, H., & Yousri, D. (2022). Optimal parameter identification of supercapacitor model using bald eagle search optimization algorithm. Journal of Energy Storage, 50,104603. 

Neumaier,A.(2004).Completesearchincontinuousglobaloptimizationandconstraint satisfaction. Acta numerica, 13,271-369. 

Neve,A. G., Kakandikar, G. M., & Kulkarni, O. (2017). Application of grasshopper optimization algorithm for constrained and unconstrained test functions. International Journal of Swarm Intelligence and Evolutionary Computation, 6(3),1-7. 

Nocedal,J.,&Wright,S.(2006).Numerical optimization:Springer Science&Business Media. 

Nouhi,B.,Darabai,N., Sareh,P.,Bayazidi,H.,Darabi,F.,&Talatahari,S. (2022).The fusion–fission optimization(FuFiO) algorithm. Scientific Reports, 12(1),1-44. 

Omar, M. B., Bingi, K., Prusty, B. R., & Ibrahim, R. (2022). Recent advances and applications of spiral dynamics optimization algorithm:Areview. Fractal and Fractional, 6(1),27. 

Omidvar, R., Parvin, H., & Rad, F. (2015). SSPCO optimization algorithm (see-see 

partridge chicks optimization). Paper presentedatthe2015FourteenthMexican InternationalConferenceonArtificialIntelligence(MICAI). 

Parouha, R. P., & Verma, P. (2022). An innovative hybrid algorithm for bound-unconstrained optimization problems and applications. Journal of Intelligent Manufacturing, 33(5),1273-1336. 

Patel, V. K., & Savsani, V. J. (2015). Heat transfer search (HTS): a novel optimization algorithm. Information sciences, 324,217-246. 

Peram, T., Veeramachaneni, K., & Mohan, C. K. (2003). Fitness-distance-ratio based particle swarm optimization. Paper presented at the Proceedings of the 2003 IEEE SwarmIntelligenceSymposium. SIS'03(Cat. No. 03EX706). 

Pham, D. T., Ghanbarzadeh,A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems Intelligent production machines and systems (pp. 454-459):Elsevier. 

Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & operations research, 34(8),2403-2435. 

Prékopa,A. (2013). Stochastic programming (Vol. 324): Springer Science& Business Media. 

Prescott-Gagnon, E., Desaulniers, G., & Rousseau, L. M. (2009).Abranch-and­

price-based large neighborhood search algorithm for the vehicle routing problem with time windows. Networks: An International Journal, 54(4), 190­204. 

Punnathanam, V., & Kotecha, P. (2016). Yin-Yang-pair Optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 54,62-79. 

Qahtan, S., Sharif, K. Y., Zaidan, A., Alsattar, H., Albahri, O., Zaidan, B., . . . Mohammed, R. (2022). Novel multi security and privacy benchmarking framework for blockchain-based IoT healthcare industry 4.0 systems. IEEE Transactions on Industrial Informatics, 18(9),6415-6423. 

Qu,B.-Y.,Liang,J.J.,&Suganthan,P.N. (2012).Nichingparticleswarmoptimization withlocalsearchfor multi-modaloptimization. Information sciences, 197,131­143. 

Rahmani,R.,&Yusof, R.(2014).Anewsimple, fastand efficientalgorithm for global optimization over continuous search-space problems: radial movement optimization. Applied Mathematics and Computation, 248,287-300. 

Rai, R., Das,A., & Dhal, K. G. (2022). Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. Evolving Systems, 1-57. 

Raja, B. D., Patel, V. K., Savsani, V. J., & Yildiz, A. R. (2022). On the comparative performance of recent swarm intelligence based algorithms for optimizationof real-life Sterling cycle operated refrigeration/liquefaction system. Artificial Intelligence Review, 1-21. 

Rajakumar, B. (2012). The Lion'sAlgorithm: a new nature-inspired search algorithm. Procedia Technology, 6,126-135. 

Rakhshani, H., & Rahati,A. (2016). Snap-drift cuckoo search:Anovel cuckoo search optimizationalgorithm. Applied Soft Computing, 52,771-794. 

Rao, R. V., Savsani, V. J., & Vakharia, D. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1),1-15. 

Rashedi,E., Nezamabadi-Pour,H.,& Saryazdi,S. (2009).GSA:agravitationalsearch algorithm. Information sciences, 179(13),2232-2248. 

Rathod, V., Tiwari, A., & Kakde, O. G. (2022). Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals. International Journal of Swarm Intelligence Research (IJSIR), 13(1),1-28. 

Rechenberg,I.(1989).Evolution strategy:Nature’swayof optimization Optimization: Methods and applications, possibilities and limitations (pp.106-126):Springer. 

Redondo,J. L. (2009). Solving competitive location problems via memetic algorithms. High performance computing approaches (Vol. 258):UniversidadAlmería. 

Rezk, H., Ferahtia, S., Sayed, E. T.,Abdelkareem, M.A., & Olabi,A. (2022). Robust parameter identificationstrategyof solidoxidefuelcellsusingbaldeaglesearch optimization algorithm. International Journal of Energy Research, 46(8), 10535-10552. 

Rezk,H.,Olabi,A.,Ferahtia, S.,&Sayed,E. T. (2022).Accurateparameter estimation methodology applied to model proton exchange membrane fuel cell. Energy, 255,124454. 

Roy, S. P., Mehta, R., Singh, A., & Roy, O. (2022). A Novel Application of BESO-Based Isolated Micro-grid with Electric Vehicle Sustainable Energy and Technological Advancements (pp. 597-609):Springer. 

Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6-7),971-983. 

Samadi-Koucheksaraee,A., Shirvani-Hosseini, S.,Ahmadianfar, I., & Gharabaghi, B. (2022). Optimization Algorithms Surpassing Metaphor Computational Intelligence for Water and Environmental Sciences (pp. 3-33):Springer. 

Sameer, F., Abu Bakar, M., Zaidan, A., & Zaidan, B. (2019). A new algorithm of modifiedbinaryparticleswarmoptimizationbasedontheGustafson-Kesselfor creditriskassessment. Neural computing and applications, 31(2),337-346. 

Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: 

theoryandapplication. Advances in engineering software, 105,30-47. 

Sarkar, T., Salauddin, M., Mukherjee,A., Shariati, M.A., Rebezov, M., Tretyak, L., . . . Lorenzo, J. M. (2022).Application of bio-inspired optimization algorithmsin foodprocessing. Current research in food science. 

Sayed, G. I.,Tharwat,A., & Hassanien,A. E. (2018). Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49(1),188-205. 

Sellmann, M., & Tierney, K. (2020). Hyper-parameterized Dialectic Search for Non­linear Box-Constrained Optimization with Heterogenous Variable Types. Paper presented at the International Conference on Learning and Intelligent Optimization. 

Serafino,L. (2021).TheNo FreeLunchTheorem:WhatAre its Main Implicationsfor theOptimizationPractice? Black Box Optimization, Machine Learning, and No-Free Lunch Theorems (pp. 357-372):Springer. 

Shahrouzi,M.,&Kaveh,A.(2022).Anefficientderivative-freeoptimizationalgorithm inspired by avian life-saving manoeuvres. Journal of Computational Science, 57,101483. 

Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015). Lightning search algorithm. Applied Soft Computing, 36,315-333. 

Sharma, M., & Kaur, P. (2021). A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem. Archives of Computational Methods in Engineering, 28(3),1103-1127. 

Sharma, S., Chakraborty, S., Saha,A. K., Nama, S., & Sahoo, S. K. (2022). mLBOA: AModified Butterfly OptimizationAlgorithm with Lagrange Interpolation for GlobalOptimization. Journal of Bionic Engineering, 1-16. 

Shen, J., & Li, J. (2010). The principle analysis of light ray optimization algorithm. Paper presented at the 2010 Second international conference on computational intelligenceandnaturalcomputing. 

Shen, X., Chang, Z., Xie, X., & Niu, S. (2022). Task Offloading Strategy ofVehicular Networks Based on Improved Bald Eagle Search Optimization Algorithm. Applied Sciences, 12(18),9308. 

Shi,Y.(2001).Particle swarm optimization: developments, applications and resources. Paper presented at the Proceedings of the 2001 congress on evolutionary computation(IEEECat. No. 01TH8546). 

Shi,Y. (2011).Brain storm optimization algorithm. Paper presentedattheInternational conferenceinswarmintelligence. 

Shi,Y.,&Eberhart,R.(1998).A modified particle swarm optimizer. Paper presentedat the 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360). 

Shiqin,Y., Jianjun, J., & Guangxing,Y. (2009). A dolphin partner optimization. Paper presentedatthe2009WRI globalcongressonintelligentsystems. 

Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6),702-713. 

Sivanandam, S., & Deepa, S. (2008). Genetic algorithms Introduction to genetic algorithms (pp. 15-37):Springer. 

Sonmez, Y., Duman, S., Kahraman, H. T., Kati, M., Aras, S., & Guvenc, U. (2022). Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem. Journal of Experimental & Theoretical Artificial Intelligence, 1-40. 

Stalmaster, M. V., & Gessaman, J. A. (1982). Food consumption and energy requirementsof captiveBaldEagles. The Journal of Wildlife Management,646­654. 

Stalmaster,M.V.,&Kaiser,J.L.(1997).Winter ecologyof baldeaglesintheNisqually River drainage,Washington. Northwest science., 71(3),214-223. 

Steczek, M., Jefimowski, W., & Szelag, A. (2020). Application of grasshopper optimization algorithm for selective harmonics elimination in low-frequency voltagesourceinverter. Energies, 13(23),6426. 

Storn,R.,&Price,K. (1997).Differentialevolution–asimpleandefficientheuristicfor global optimization over continuous spaces. Journal of global optimization, 11(4),341-359. 

Stripinis, L., & Paulavicius, R. (2022). An extensive numerical benchmark study of deterministic vs. stochastic derivative-free global optimization algorithms. arXiv preprint arXiv:2209.05759. 

Subashini, P., Dhivyaprabha, T., & Krishnaveni, M. (2017). Synergistic fibroblast optimization Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 285-294):Springer. 

Sudholt, D. (2020). The benefits of population diversity in evolutionary algorithms: a surveyof rigorousruntimeanalyses. Theory of Evolutionary Computation,359­

404. 

Suganthan,P. N.,Hansen,N.,Liang,J. J.,Deb,K.,Chen,Y.-P.,Auger,A.,&Tiwari,S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special sessiononreal-parameter optimization. KanGAL report, 2005005(2005),2005. 

Sulaiman,M.,Salhi,A.,Selamoglu,B.I.,&Kirikchi,O.B.(2014).Aplantpropagation algorithm for constrained engineering optimisation problems. Mathematical Problems in Engineering, 2014. 

Sur, C., Sharma, S., & Shukla,A. (2013). Egyptian vulture optimization algorithm–a new nature inspired meta-heuristics for knapsack problem. Paper presented at the The 9th international conference on computing and informationtechnology (IC2IT2013). 

Talatahari, S., Veladi, H., Azizi, M., Moutabi-Alavi, A., & Rahnema, S. (2022). Optimumstructuraldesignof full-scalesteelbuildingsusingdrift-tribe-charged systemsearch.Earthquake Engineering and Engineering Vibration, 21(3),825­842. 

Taleb,S. M., Meraihi,Y.,Gabis,A. B., Mirjalili, S.,Zaguia,A.,& Ramdane-Cherif,A. (2022). Solving the mesh router nodes placement in wireless mesh networks usingcoyoteoptimizationalgorithm. IEEE Access. 

Talpur,N.,Abdulkadir,S.J.,Alhussian,H.,Hasan,M.H.,&Abdullah,M.H.A.(2022). OptimizingDeepNeuro-FuzzyClassifier withANovelEvolutionaryArithmetic OptimizationAlgorithm. Journal of Computational Science,101867. 

Tariq, M. A., Shami, U. T., Fakhar, M. S., Kashif, S. A. R.,Abbas, G., Ullah, N., . . . Farrag, M. E. (2022). Dragonfly Algorithm-Based Optimization for Selective Harmonics Elimination in Cascaded H-Bridge Multilevel Inverters with 

StatisticalComparison. Energies, 15(18),6826. 

Tayeb, F. B.-S., Bessedik, M., Benbouzid, M., Cheurfi, H., & Blizak, A. (2017). Research on permutation flow-shop scheduling problem based on improved genetic immune algorithm with vaccinated offspring. Procedia computer science, 112,427-436. 

Thawkar, S., Sharma, S., Khanna, M., & kumar Singh, L. (2021). Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm andAntLionOptimizer. Computers in Biology and Medicine, 139,104968. 

Ting, T., Man, K. L., Guan, S.-U., Nayel, M., & Wan, K. (2012). Weightless swarm algorithm (WSA) for dynamic optimization problems. Paper presented at the IFIPInternationalConferenceonNetwork andParallelComputing. 

Todd,C.,Young,L.,Owen Jr, R., &Gramlich,F. (1982).Food habitsof baldeaglesin Maine. The Journal of Wildlife Management,636-645. 

Tong, Y., & Cheng, X. (2022). Location of Logistics Distribution Center Based on ImprovedBaldEagleAlgorithm. Sustainability, 14(15),9036. 

Torabi,S.,&Safi-Esfahani,F. (2018).Improvedravenroostingoptimizationalgorithm (IRRO). Swarm and Evolutionary Computation, 40,144-154. 

Törn,A., &Zilinskas,A. (1989).Globaloptimization. 

Tuerxun, W., Xu, C., Guo, H., Guo, L., Zeng, N., & Gao, Y. (2022).A Wind Power ForecastingModelUsingLSTMOptimizedbytheModified BaldEagleSearch Algorithm. Energies, 15(6),2031. 

VATANSEVER, F., & HACIISKENDEROGLU, E. (2022). PID TUNING WITH UP­TO-DATE METAHEURISTIC ALGORITHMS. Uludag Üniversitesi Mühendislik Fakültesi Dergisi, 27(2),573-584. 

Vesselinova, N., Steinert, R., Perez-Ramirez, D. F., & Boman, M. (2020). Learning combinatorial optimization on graphs: A survey with applications to networking. IEEE Access, 8,120388-120416. 

Wang, G.-G., Deb, S., & Coelho, L. D. S. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. International journal of bio-inspired computation, 12(1),1-22. 

Wang, G.-G., Deb, S., & Cui, Z. (2015). Monarch butterfly optimization. Neural computing and applications, 31(7),1995-2014. 

Wang, G.-G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural computing and applications, 31(7),1995-2014. 

Wang, P., Zhu, Z., & Huang, S. (2013). Seven-spot ladybird optimization: anovel and efficient metaheuristic algorithm for numerical optimization. The Scientific World Journal, 2013. 

Wang, Y., Zhang, W., Sun, J., Wang, L., Song, X., & Zhao, X. (2022). Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm. Computational and Mathematical Methods in Medicine, 2022. 

Whitley, D. (2001). An overview of evolutionary algorithms: practical issues and commonpitfalls. Information and software technology, 43(14),817-831. 

Wright, S. J. (1999). Continuous optimization (nonlinear and linear programming). Foundations of Computer-Aided Process Design. 

Wu, J., Hu,Y., Wu, D., &Yang, Z. (2022).AnAquatic Product Price Forecast Model UsingVMD-IBES-LSTM HybridApproach. Agriculture, 12(8),1185. 

Wu,Y.-C.,Lee,W.-P.,&Chien,C.-W. (2011). Modified the performance of differential evolution algorithm with dual evolution strategy. Paper presented at the Internationalconferenceonmachinelearningandcomputing. 

Yadav, A. (2019). AEFA: Artificial electric field algorithm for global optimization. Swarm and Evolutionary Computation, 48,93-108. 

Yang, H., Wang, Z., Zhang, L., & Cheng, X. (2022). IoT botnet detection with feature reconstruction and interval optimization. International Journal of Intelligent Systems. 

Yang,X.-S. (2010a). Nature-inspired metaheuristic algorithms:Luniver press. 

Yang, X.-S. (2010b). A new metaheuristic bat-inspired algorithm Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74):Springer. 

Yang, X.-S. (2012). Flower pollination algorithm for global optimization. Paper presented at the International conference on unconventional computing and naturalcomputation. 

Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights. Paper presented at the 2009Worldcongressonnature&biologicallyinspired computing(NaBIC). 

Yang, X.-S., Deb, S., & Mishra, S. K. (2018). Multi-species cuckoo search algorithm for globaloptimization. Cognitive Computation, 10(6),1085-1095. 

Yang,X.-S.,Karamanoglu,M.,&He,X.(2014).Flower pollinationalgorithm:anovel approach for multiobjective optimization. Engineering optimization, 46(9), 1222-1237. 

Yang, X. (2009). Firefly algorithm, levy flights and global optimization. Bramer M, EllisR,PetridisM(eds) ResearchanddevelopmentinintelligentsystemsXXVI 2009:Springer,Berlin. 

Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: a novel approach for global engineeringoptimization. Engineering computations. 

Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE transactions on evolutionary computation, 3(2),82-102. 

Yazdani,M., &Jolai, F. (2016).Lionoptimization algorithm (LOA):a nature-inspired metaheuristic algorithm. Journal of computational design and engineering, 3(1),24-36. 

Yildiz,A. R. (2009).An effectivehybridimmune-hill climbingoptimizationapproach for solvingdesign andmanufacturingoptimizationproblemsinindustry. 

Yin, S., Luo, Q., Du,Y., & Zhou,Y. (2022). DTSMA: Dominant swarm with adaptive t-distributionmutation-basedslimemouldalgorithm.Mathematical Biosciences and Engineering, 19(3),2240-2285. 

Zaidan,A.,Atiya, B.,AbuBakar, M., & Zaidan,B. (2019).Anew hybridalgorithmof simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment. Neural computing and applications, 31(6),1823-1834. 

Zervoudakis,K.,&Tsafarakis,S. (2022).Aglobaloptimizer inspiredfromthesurvival strategiesof flyingfoxes. Engineering with Computers, 1-34. 

Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristicalgorithmfor solvingengineeringoptimizationproblems.Applied Mathematical Modelling, 63,464-490. 

Zhang, W., Luo, Q., & Zhou, Y. (2009). A method for training RBF neural networks based on population migration algorithm. Paper presented at the 2009 International Conference on Artificial Intelligence and Computational Intelligence. 

Zhang, X., Chen, W., & Dai, C. (2008). Application of oriented search algorithm in reactive power optimization of power system. Paper presentedatthe2008Third International conference on electric utility deregulation and restructuring and power technologies. 

Zhang, Y., Zhou, Y., Zhou, G., & Luo, Q. (2022).An Effective Multi-Objective Bald EagleSearchAlgorithmfor SolvingEngineeringDesignProblems. Available at SSRN 4172705. 

Zhang, Y., Zhou, Y., Zhou, G., Luo, Q., & Zhu, B. (2022). A Curve Approximation Approach Using Bio-inspired Polar Coordinate Bald Eagle Search Algorithm. International Journal of Computational Intelligence Systems, 15(1),1-25. 

Zhao, Q., & Li, C. (2020). Two-stage multi-swarm particle swarm optimizer for unconstrained and constrained global optimization. IEEE Access, 8, 124905­124927. 

Zhou, J., Xu, Z., & Wang, S. (2022).Anovel dual-scale ensemble learning paradigm with error correction for predicting daily ozone concentration based on multi-decomposition process and intelligent algorithm optimization, and its application in heavily polluted regions of China. Atmospheric Pollution Research, 13(2),101306.