A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO's mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications.

Department of Electrical and Computer Engineering University of Calgary Calgary AB T2N 1N4 Canada

Department of Mathematics Faculty of Science University of Hradec Králové 500 03 Hradec Králové Czech Republic

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Zhao S., Zhang T., Ma S., Chen M. Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Eng. Appl. Artif. Intell. 2022;114:105075. doi: 10.1016/j.engappai.2022.105075. DOI

Jahani E., Chizari M. Tackling global optimization problems with a novel algorithm–Mouth Brooding Fish algorithm. Appl. Soft Comput. 2018;62:987–1002. doi: 10.1016/j.asoc.2017.09.035. DOI

Sergeyev Y.D., Kvasov D., Mukhametzhanov M. On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget. Sci. Rep. 2018;8:453. doi: 10.1038/s41598-017-18940-4. PubMed DOI PMC

Liberti L., Kucherenko S. Comparison of deterministic and stochastic approaches to global optimization. Int. Trans. Oper. Res. 2005;12:263–285. doi: 10.1111/j.1475-3995.2005.00503.x. DOI

Koc I., Atay Y., Babaoglu I. Discrete tree seed algorithm for urban land readjustment. Eng. Appl. Artif. Intell. 2022;112:104783. doi: 10.1016/j.engappai.2022.104783. DOI

Dehghani M., Trojovská E., Trojovský P. A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Sci. Rep. 2022;12:9924. doi: 10.1038/s41598-022-14225-7. PubMed DOI PMC

Zeidabadi F.-A., Dehghani M., Trojovský P., Hubálovský Š., Leiva V., Dhiman G. Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems. Comput. Mater. Contin. 2022;72:399–416. doi: 10.32604/cmc.2022.024736. DOI

de Armas J., Lalla-Ruiz E., Tilahun S.L., Voß S. Similarity in metaheuristics: A gentle step towards a comparison methodology. Nat. Comput. 2022;21:265–287. doi: 10.1007/s11047-020-09837-9. DOI

Trojovská E., Dehghani M., Trojovský P. Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm. IEEE Access. 2022;10:49445–49473. doi: 10.1109/ACCESS.2022.3172789. DOI

Dehghani M., Montazeri Z., Dehghani A., Malik O.P., Morales-Menendez R., Dhiman G., Nouri N., Ehsanifar A., Guerrero J.M., Ramirez-Mendoza R.A. Binary spring search algorithm for solving various optimization problems. Appl. Sci. 2021;11:1286. doi: 10.3390/app11031286. DOI

Wolpert D.H., Macready W.G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1997;1:67–82. doi: 10.1109/4235.585893. DOI

Dorigo M., Maniezzo V., Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 1996;26:29–41. doi: 10.1109/3477.484436. PubMed DOI

Karaboga D., Basturk B. International Fuzzy Systems Association World Congress. Springer; Berlin/Heidelberg, Germany: 2007. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems; pp. 789–798.

Kennedy J., Eberhart R. Particle swarm optimization; Proceedings of the ICNN’95-International Conference on Neural Networks; Perth, WA, Australia. 27 November–1 December 1995; Perth, WA, Australia: IEEE; 1995. pp. 1942–1948.

Mirjalili S., Mirjalili S.M., Lewis A. Grey Wolf Optimizer. Adv. Eng. Softw. 2014;69:46–61. doi: 10.1016/j.advengsoft.2013.12.007. DOI

Jiang Y., Wu Q., Zhu S., Zhang L. Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems. Expert Syst. Appl. 2022;188:116026. doi: 10.1016/j.eswa.2021.116026. DOI

Abdollahzadeh B., Gharehchopogh F.S., Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput. Ind. Eng. 2021;158:107408. doi: 10.1016/j.cie.2021.107408. DOI

Faramarzi A., Heidarinejad M., Mirjalili S., Gandomi A.H. Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Syst. Appl. 2020;152:113377. doi: 10.1016/j.eswa.2020.113377. DOI

Braik M., Hammouri A., Atwan J., Al-Betar M.A., Awadallah M.A. White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl. Based Syst. 2022;234:108457. doi: 10.1016/j.knosys.2022.108457. DOI

Abualigah L., Abd Elaziz M., Sumari P., Geem Z.W., Gandomi A.H. Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst. Appl. 2022;191:116158. doi: 10.1016/j.eswa.2021.116158. DOI

Chopra N., Ansari M.M. Golden Jackal Optimization: A Novel Nature-Inspired Optimizer for Engineering Applications. Expert Syst. Appl. 2022;198:116924. doi: 10.1016/j.eswa.2022.116924. DOI

Mirjalili S., Lewis A. The whale optimization algorithm. Adv. Eng. Softw. 2016;95:51–67. doi: 10.1016/j.advengsoft.2016.01.008. DOI

Hashim F.A., Houssein E.H., Hussain K., Mabrouk M.S., Al-Atabany W. Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 2022;192:84–110. doi: 10.1016/j.matcom.2021.08.013. DOI

Kaur S., Awasthi L.K., Sangal A.L., Dhiman G. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Eng. Appl. Artif. Intell. 2020;90:103541. doi: 10.1016/j.engappai.2020.103541. DOI

Goldberg D.E., Holland J.H. Genetic Algorithms and Machine Learning. Mach. Learn. 1988;3:95–99. doi: 10.1023/A:1022602019183. DOI

Storn R., Price K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 1997;11:341–359. doi: 10.1023/A:1008202821328. DOI

Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science. 1983;220:671–680. doi: 10.1126/science.220.4598.671. PubMed DOI

Rashedi E., Nezamabadi-Pour H., Saryazdi S. GSA: A gravitational search algorithm. Inf. Sci. 2009;179:2232–2248. doi: 10.1016/j.ins.2009.03.004. DOI

Dehghani M., Montazeri Z., Dhiman G., Malik O., Morales-Menendez R., Ramirez-Mendoza R.A., Dehghani A., Guerrero J.M., Parra-Arroyo L. A spring search algorithm applied to engineering optimization problems. Appl. Sci. 2020;10:6173. doi: 10.3390/app10186173. DOI

Dehghani M., Samet H. Momentum search algorithm: A new meta-heuristic optimization algorithm inspired by momentum conservation law. SN Appl. Sci. 2020;2:1720. doi: 10.1007/s42452-020-03511-6. DOI

Eskandar H., Sadollah A., Bahreininejad A., Hamdi M. Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 2012;110:151–166. doi: 10.1016/j.compstruc.2012.07.010. DOI

Hashim F.A., Hussain K., Houssein E.H., Mabrouk M.S., Al-Atabany W. Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Appl. Intell. 2021;51:1531–1551. doi: 10.1007/s10489-020-01893-z. DOI

Hashim F.A., Houssein E.H., Mabrouk M.S., Al-Atabany W., Mirjalili S. Henry gas solubility optimization: A novel physics-based algorithm. Future Gener. Comput. Syst. 2019;101:646–667. doi: 10.1016/j.future.2019.07.015. DOI

Faramarzi A., Heidarinejad M., Stephens B., Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowl. Based Syst. 2020;191:105190. doi: 10.1016/j.knosys.2019.105190. DOI

Pereira J.L.J., Francisco M.B., Diniz C.A., Oliver G.A., Cunha Jr S.S., Gomes G.F. Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization. Expert Syst. Appl. 2021;170:114522. doi: 10.1016/j.eswa.2020.114522. DOI

Wei Z., Huang C., Wang X., Han T., Li Y. Nuclear reaction optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access. 2019;7:66084–66109. doi: 10.1109/ACCESS.2019.2918406. DOI

Cuevas E., Oliva D., Zaldivar D., Pérez-Cisneros M., Sossa H. Circle detection using electro-magnetism optimization. Inf. Sci. 2012;182:40–55. doi: 10.1016/j.ins.2010.12.024. DOI

Hatamlou A. Black hole: A new heuristic optimization approach for data clustering. Inf. Sci. 2013;222:175–184. doi: 10.1016/j.ins.2012.08.023. DOI

Mirjalili S., Mirjalili S.M., Hatamlou A. Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Comput. Appl. 2016;27:495–513. doi: 10.1007/s00521-015-1870-7. DOI

Kaveh A., Dadras A. A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Adv. Eng. Softw. 2017;110:69–84. doi: 10.1016/j.advengsoft.2017.03.014. DOI

Rao R.V., Savsani V.J., Vakharia D. Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput. Aided Des. 2011;43:303–315. doi: 10.1016/j.cad.2010.12.015. DOI

Dehghani M., Mardaneh M., Guerrero J.M., Malik O.P., Ramirez-Mendoza R.A., Matas J., Vasquez J.C., Parra-Arroyo L. A new “Doctor and Patient” optimization algorithm: An application to energy commitment problem. Appl. Sci. 2020;10:5791. doi: 10.3390/app10175791. DOI

Dehghani M., Mardaneh M., Malik O. FOA:‘Following’Optimization Algorithm for solving Power engineering optimization problems. J. Oper. Autom. Power Eng. 2020;8:57–64.

Ayyarao T.L., RamaKrishna N., Elavarasam R.M., Polumahanthi N., Rambabu M., Saini G., Khan B., Alatas B. War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization. IEEE Access. 2022;10:25073–25105. doi: 10.1109/ACCESS.2022.3153493. DOI

Dehghani M., Trojovský P. Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization. Sensors. 2021;21:4567. doi: 10.3390/s21134567. PubMed DOI PMC

Braik M., Ryalat M.H., Al-Zoubi H. A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves. Neural Comput. Appl. 2022;34:409–455. doi: 10.1007/s00521-021-06392-x. DOI

Mohamed A.W., Hadi A.A., Mohamed A.K. Gaining-sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm. Int. J. Mach. Learn. Cybern. 2020;11:1501–1529. doi: 10.1007/s13042-019-01053-x. DOI

Al-Betar M.A., Alyasseri Z.A.A., Awadallah M.A., Abu Doush I. Coronavirus herd immunity optimizer (CHIO) Neural Comput. Appl. 2021;33:5011–5042. doi: 10.1007/s00521-020-05296-6. PubMed DOI PMC

Dehghani M., Montazeri Z., Saremi S., Dehghani A., Malik O.P., Al-Haddad K., Guerrero J.M. HOGO: Hide objects game optimization. Int. J. Intell. Eng. Syst. 2020;13:4. doi: 10.22266/ijies2020.0831.19. DOI

Dehghani M., Montazeri Z., Malik O.P., Ehsanifar A., Dehghani A. OSA: Orientation search algorithm. Int. J. Ind. Electron. Control. Optim. 2019;2:99–112.

Moghdani R., Salimifard K. Volleyball premier league algorithm. Appl. Soft Comput. 2018;64:161–185. doi: 10.1016/j.asoc.2017.11.043. DOI

Dehghani M., Mardaneh M., Guerrero J.M., Malik O., Kumar V. Football game based optimization: An application to solve energy commitment problem. Int. J. Intell. Eng. Syst. 2020;13:514–523. doi: 10.22266/ijies2020.1031.45. DOI

DEHGHANI M., MONTAZERI Z., MALIK O.P. DGO: Dice game optimizer. Gazi Univ. J. Sci. 2019;32:871–882. doi: 10.35378/gujs.484643. DOI

Doumari S.A., Givi H., Dehghani M., Malik O.P. Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems. Int. J. Intell. Eng. Syst. 2021;14:545–554. doi: 10.22266/ijies2021.0630.46. DOI

Zeidabadi F.A., Dehghani M. POA: Puzzle Optimization Algorithm. Int. J. Intell. Eng. Syst. 2022;15:273–281.

Hsiou A.S., Winck G.R., Schubert B.W., Avilla L. On the presence of Eunectes murinus (Squamata, Serpentes) from the late Pleistocene of northern Brazil. Rev. Bras. De Paleontol. 2013;16:77–82. doi: 10.4072/rbp.2013.1.06. DOI

Rivas J.A. Ph.D. Thesis. The University of Tennessee; Knoxville, TN, USA: 1999. The Life History of the Green Anaconda (Eunectes murinus), with Emphasis on Its Reproductive Biology.

Rivas J.A., Burghardt G.M. Understanding sexual size dimorphism in snakes: Wearing the snake’s shoes. Anim. Behav. 2001;62:F1–F6. doi: 10.1006/anbe.2001.1755. DOI

Pope C.H. The Giant Snakes: The Natural History of the Boa Constrictor, the Anaconda, and the Largest Pythons, Including Comparative Facts about Other Snakes and Basic Information on Reptiles in General. Knopf; New York, NY, USA: 1961.

Harvey D. Smithsonian Super Nature Encyclopedia. Dorling Kindersley Publishing; London, UK: 2012.

Thomas O., Allain S. Review of prey taken by anacondas (Squamata, Boidae: Eunectes) Reptiles Amphib. 2021;28:329–334. doi: 10.17161/randa.v28i2.15504. DOI

Strimple P. The Green Anaconda Eunectes murinus (Linnaeus) Liyyeratura Serpentium. 1993;13:46–50.

Burton M., Burton R. International Wildlife Encyclopedia. Volume 1 Marshall Cavendish; New York, NY, USA: 2002.

Awad N., Ali M., Liang J., Qu B., Suganthan P., Definitions P. Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization. Nanyang Technological University; Singapore: 2016. Technology Report.

Price K.V., Awad N.H., Ali M.Z., Suganthan P.N. Problem Definitions and Evaluation Criteria for the 100-Digit Challenge Special Session and Competition on Single Objective Numerical Optimization. Nanyang Technological University; Singapore: 2018.

Wilcoxon F. Breakthroughs in Statistics. Springer; Berlin/Heidelberg, Germany: 1992. Individual comparisons by ranking methods; pp. 196–202.

Das S., Suganthan P.N. Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems. Jadavpur University; Kolkata, India: Nanyang Technological University; Singapore: 2010. pp. 341–359.

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