Abstract
Original language | English |
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Journal | Scientific Reports |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2022 |
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The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems : Scientific Reports. / Akbari, M.A.; Zare, M.; Azizipanah-abarghooee, R. et al.
In: Scientific Reports, Vol. 12, No. 1, 2022.Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems
T2 - Scientific Reports
AU - Akbari, M.A.
AU - Zare, M.
AU - Azizipanah-abarghooee, R.
AU - Mirjalili, S.
AU - Deriche, M.
N1 - Export Date: 11 July 2022 Correspondence Address: Deriche, M.; Artificial Intelligence Research Centre, United Arab Emirates; email: m.deriche@ajman.ac.ae References: Sergeyev, Y.D., Kvasov, D.E., Mukhametzhanov, M.S., On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget (2018) Sci. Rep., 8, pp. 1-9. , COI: 1:CAS:528:DC%2BC1cXhsF2lur%2FN; Luo, X., Liu, H., Gou, G., Xia, Y., Zhu, Q., A parallel matrix factorization based recommender by alternating stochastic gradient decent (2012) Eng. Appl. Artif. Intell., 25, pp. 1403-1412; Lu, T., Liu, S.-T., Fuzzy nonlinear programming approach to the evaluation of manufacturing processes (2018) Eng. Appl. Artif. Intell., 72, pp. 183-189; Koc, I., Atay, Y., Babaoglu, I., Discrete tree seed algorithm for urban land readjustment (2022) Eng. Appl. Artif. Intell., 112, p. 104783; Spall, J.C., (2005) Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, 65. , Wiley; Cruz, Y.J., Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process (2021) Comput. Ind., 133, p. 103530; Haber, R.E., Beruvides, G., Quiza, R., Hernandez, A., A simple multi-objective optimization based on the cross-entropy method (2017) IEEE Access, 5, pp. 22272-22281; Alba, E., Dorronsoro, B., The exploration/exploitation tradeoff in dynamic cellular genetic algorithms (2005) IEEE Trans. Evol. Comput., 9, pp. 126-142; Wang, J.-S., Li, S.-X., An improved grey wolf optimizer based on differential evolution and elimination mechanism (2019) Sci. Rep., 9, pp. 1-21; Lozano, M., García-Martínez, C., Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report (2010) Comput. Oper. Res., 37, pp. 481-497; Kirkpatrick, S., Optimization by simulated annealing: quantitative studies (1984) J. Stat. Phys., 34, pp. 975-986; Wolpert, D.H., Macready, W.G., No free lunch theorems for optimization (1997) IEEE Trans. Evol. Comput., 1, pp. 67-82; Singh, P., Dhiman, G., Kaur, A., A quantum approach for time series data based on graph and Schrödinger equations methods (2018) Mod. Phys. Lett. A, 33, p. 1850208; Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., Optimization by simulated annealing (1983) Science, 220, pp. 671-680. , COI: 1:STN:280:DC%2BC3cvktFWjtw%3D%3D, PID: 17813860; Xing, B., Gao, W.-J., (2014) Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, , Springer; Holland, J.H., Genetic algorithms (1992) Sci. Am., 267, pp. 66-73; Koza, J.R., Genetic programming as a means for programming computers by natural selection (1994) Stat. Comput., 4, pp. 87-112; Yao, X., Liu, Y., Lin, G., Evolutionary programming made faster (1999) IEEE Trans. Evol. Comput., 3, pp. 82-102; Rechenberg, I., (1994) Evolutionsstrategie'94, , Frommann-holzboog; Simon, D., Biogeography-based optimization (2008) IEEE Trans. Evol. Comput., 12, pp. 702-713; Storn, R., Price, K., Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces (1997) J. Glob. Optim., 11, pp. 341-359; Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., GSA: a gravitational search algorithm (2009) Inf. Sci., 179, pp. 2232-2248; Erol, O.K., Eksin, I., A new optimization method: big bang–big crunch (2006) Adv. Eng. Softw., 37, pp. 106-111; Kaveh, A., Talatahari, S., A novel heuristic optimization method: charged system search (2010) Acta Mech., 213, pp. 267-289; Birbil, Ş.İ., Fang, S.-C., An electromagnetism-like mechanism for global optimization (2003) J. Glob. Optim., 25, pp. 263-282; Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M., Water cycle algorithm: a novel metaheuristic optimization method for solving constrained engineering optimization problems (2012) Comput. Struct., 110, pp. 151-166; Boettcher, S., Percus, A.G., Optimization with extremal dynamics (2002) Complexity, 8, pp. 57-62; Kaveh, A., Khayatazad, M., A new meta-heuristic method: ray optimization (2012) Comput. Struct., 112, pp. 283-294; Formato, R.A., Central force optimization (2007) Prog. Electromagn. Res., 77, pp. 425-491; Hosseini, H.S., (2007) IEEE Congress on Evolutionary Computation, pp. 3226-3231. , IEEE; Bing, L., Weisun, J., Chaos optimization method and its application (1997) Control Theory Appl., 14, pp. 613-615; Shah-Hosseini, H., Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation (2011) Int. J. Comput. Sci. Eng., 6, pp. 132-140; Rabanal, P., Rodríguez, I., Rubio, F., In International Conference on Unconventional Computation., pp. 163-177. , (Springer); Alatas, B., ACROA: artificial chemical reaction optimization algorithm for global optimization (2011) Expert Syst. Appl., 38, pp. 13170-13180; Abdechiri, M., Meybodi, M.R., Bahrami, H., Gases Brownian motion optimization: an algorithm for optimization (GBMO) (2013) Appl. Soft Comput., 13, pp. 2932-2946; Irizarry, R., LARES: an artificial chemical process approach for optimization (2004) Evol. Comput., 12, pp. 435-459. , PID: 15768524; Kennedy, J., Eberhart, R., In Proceedings of Icnn'95-International Conference on Neural Networks, pp. 1942-1948; Colorni, A., Dorigo, M., Maniezzo, V., . in Proceedings of the First European Conference on Artificial Life., pp. 134-142; Karaboga, D., Basturk, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm J (2007) . Glob. Optim., 39, pp. 459-471; (2009) In World Congress on Nature & Biologically Inspired Computing (Nabic, pp. 210-214. , IEEE; Mirjalili, S., Mirjalili, S.M., Lewis, A., Grey Wolf optimizer (2014) Adv. Eng. Softw., 69, pp. 46-61; Yang, X.-S., In International Symposium on Stochastic Algorithms, pp. 169-178. , Springer; Passino, K.M., Biomimicry of bacterial foraging for distributed optimization and control (2002) IEEE Control Syst. Mag., 22, pp. 52-67; Mirjalili, S., Lewis, A., The whale optimization algorithm (2016) Adv. Eng. Softw., 95, pp. 51-67; Yang, X.-S., (2010) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)., pp. 65-74. , Springer; Eusuff, M.M., Lansey, K.E., Optimization of water distribution network design using the shuffled frog leaping algorithm (2003) J. Water Resour. Plan. Manag., 129, pp. 210-225; Pham, D.T., (2006) Intelligent Production Machines and Systems, pp. 454-459. , Elsevier; Mirjalili, S., Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm (2015) Knowl. Based Syst., 89, pp. 228-249; Gandomi, A.H., Alavi, A.H., Krill herd: a new bio-inspired optimization algorithm (2012) Commun. Nonlinear Sci. Numer. Simul., 17, pp. 4831-4845; Mirjalili, S., The ant lion optimizer (2015) Adv. Eng. Softw., 83, pp. 80-98; Pan, W.-T., A new fruit fly optimization algorithm: taking the financial distress model as an example (2012) Knowl. Based Syst., 26, pp. 69-74; Krishnanand, K., Ghose, D., Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions (2009) Swarm Intell., 3, pp. 87-124; Goudhaman, M., Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm (2018) Int. J. Eng. Technol., 7, pp. 1804-1811; Saravanan, D., Paul, P.V., Janakiraman, S., Dumka, A., Jayakumar, L., A new bio-inspired algorithm based on the hunting behavior of cheetah (2020) Int. J. Inf. Technol. Project Manag. (IJITPM), 11, pp. 13-30; Klein, C.E., Mariani, V.C., Dos Santos Coelho, L., (2018) Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm, , In ESANN 685–690; O’Brien, S.J., Johnson, W.E., Driscoll, C.A., Dobrynin, P., Marker, L., Conservation genetics of the cheetah: lessons learned and new opportunities (2017) J. Hered., 108, pp. 671-677. , PID: 28821181; Marker, L., Boast, L.K., Schmidt-Küntzel, A., (2018) Cheetahs: Biology and Conservation, , Academic Press; Krausman, P.R., Morales, S.M., Acinonyx jubatus (2005) Mamm. Species, 2005, pp. 1-6; Estes, R.D., (2012) The Behavior Guide to African Mammals: Including Hoofed Mammals, Carnivores, , Primates. University of California Press; (2013) In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5506-5511. , IEEE; Aarde, R.J., Dyk, A., Inheritance of the king coat colour pattern in cheetahs Acinonyx jubatus (1986) J. Zool, 209, pp. 573-578; Phillips, J.A., Bone consumption by cheetahs at undisturbed kills: evidence for a lack of focal-palatine erosion (1993) J. Mammal., 74, pp. 487-492; https://pixabay.com; Dhiman, G., Kumar, V., Emperor penguin optimizer: a bio-inspired algorithm for engineering problems (2018) Knowl. Based Syst., 159, pp. 20-50; Li, S., Chen, H., Wang, M., Heidari, A.A., Mirjalili, S., Slime mould algorithm: a new method for stochastic optimization (2020) Future Gener. Comput. Syst., 111, pp. 300-323; Rao, R., Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems (2016) Int. J. Ind. Eng. Comput., 7, pp. 19-34; Patel, V.K., Savsani, V.J., Heat transfer search (HTS): a novel optimization algorithm (2015) Inf. Sci., 324, pp. 217-246; Shi, Y., Eberhart, R., In IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360)., pp. 69-73. , IEEE, 1998); Qiang, J., Mitchell, C., Qiang, A., (2016) In IEEE Congress on Evolutionary Computation (CEC)., pp. 4061-4068; Fu, L., Zhu, H., Zhang, C., Ouyang, H., Li, S., Hybrid harmony search differential evolution algorithm (2021) IEEE Access, 9, pp. 21532-21555; Wang, Y., Cai, Z., Zhang, Q., Differential evolution with composite trial vector generation strategies and control parameters (2011) IEEE Trans. Evol. Comput., 15, pp. 55-66. , COI: 1:CAS:528:DC%2BC38Xht1yks78%3D; Jian, J.-R., Chen, Z.-G., Zhan, Z.-H., Zhang, J., Region encoding helps evolutionary computation evolve faster: a new solution encoding scheme in particle swarm for large-scale optimization (2021) IEEE Trans. Evol. Comput., 25, pp. 779-793; Wang, Z.-J., Dynamic group learning distributed particle swarm optimization for large-scale optimization and its application in cloud workflow scheduling (2019) IEEE Trans. Cybern., 50, pp. 2715-2729. , PID: 31545753; Li, X., Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization (2013) Gene, 7, p. 8; Sun, J., Fang, W., Wang, D., Xu, W., Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method (2009) Energy Convers. Manag., 50, pp. 2967-2975; Panigrahi, B., Pandi, V.R., Bacterial foraging optimisation: Nelder-Mead hybrid algorithm for economic load dispatch (2008) IET Gener. Transm. Distrib., 2, pp. 556-565; Kumar, M., Dhillon, J.S., A conglomerated ion-motion and crisscross search optimizer for electric power load dispatch (2019) Appl. Soft Comput., 83, p. 105641; Nee Dey, S.H., Teaching learning based optimization for different economic dispatch problems (2014) Sci. Iran., 21, pp. 870-884; Qin, Q., Cheng, S., Chu, X., Lei, X., Shi, Y., Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization (2017) Appl. Soft Comput., 59, pp. 229-242; Singh, N.J., Dhillon, J., Kothari, D., Synergic predator-prey optimization for economic thermal power dispatch problem (2016) Appl. Soft Comput., 43, pp. 298-311; Xiong, G., Shi, D., Duan, X., Multi-strategy ensemble biogeography-based optimization for economic dispatch problems (2013) Appl. Energy, 111, pp. 801-811; Kapelinski, K., Neto, J.P.J., dos Santos, E.M., Firefly algorithm with non-homogeneous population: a case study in economic load dispatch problem J (2021) . Oper. Res. Soc., 72, pp. 519-534; Yu, J., Kim, C.-H., Rhee, S.-B., Clustering cuckoo search optimization for economic load dispatch problem (2020) Neural Comput. Appl., 32, pp. 16951-16969; Xiong, G., Shi, D., Orthogonal learning competitive swarm optimizer for economic dispatch problems (2018) Appl. Soft Comput., 66, pp. 134-148; Zakian, P., Kaveh, A., Economic dispatch of power systems using an adaptive charged system search algorithm (2018) Appl. Soft Comput., 73, pp. 607-622
PY - 2022
Y1 - 2022
N2 - Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co. © 2022, The Author(s).
AB - Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co. © 2022, The Author(s).
U2 - 10.1038/s41598-022-14338-z
DO - 10.1038/s41598-022-14338-z
M3 - Article
VL - 12
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
ER -