×

A novel version of slime mould algorithm for global optimization and real world engineering problems. Enhanced slime mould algorithm. (English) Zbl 1540.90314

Summary: The slime mould algorithm is a stochastic optimization algorithm based on the oscillation mode of nature’s slime mould, and it has effective convergence. On the other hand, it gets stuck at the local optimum and struggles to find the global optimum. Location updates of slime moulds are very important in terms of convergence to optimum. In this study, the position updates of the sine cosine algorithm are combined with the slime mould algorithm. In these updates, besides the existing sine cosine algorithm, different types of sine cosine algorithmic transformations are used and the oscillation processes of the slime moulds are also modified. In the mathematical model of the slime mould algorithm, the arctanh function that stacks two random slime moulds in a certain interval has been replaced by a novel modified sigmoid function. The proposed function is presented with its theoretical derivations based on Schwarz lemma. According to experimental results, it has been observed that the exploration and exploitation capabilities of the proposed algorithm are highly effective. In the study, sine cosine trigonometric functions have been used while updating the position in slime mould algorithm. The performance of the presented algorithm has been considered for fifty benchmark functions and has also been tested on cantilever beam design, pressure vessel design, 3-bar truss and speed reducer real world problems. Accordingly, it is possible to conclude that the proposed hybrid algorithm has better ability to escape from local optima with faster convergence than standard sine cosine and slime mould algorithms.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI

References:

[1] Abdel-Basset, M.; Chang, V.; Mohamed, R., Hsma_Woa: A hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest x-ray images, Appl. Soft Comput., 95, Article 106642 pp. (2020)
[2] Abdollahzadeh, B.; Gharehchopogh, F. S.; Mirjalili, S., African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Comput. Ind. Eng., 158, Article 107408 pp. (2021)
[3] Abdollahzadeh, B.; Soleimanian Gharehchopogh, F.; Mirjalili, S., Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems, Int. J. Intell. Syst., 36, 10, 5887-5958 (2021)
[4] 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., 191, Article 116158 pp. (2022)
[5] Abualigah, L.; Diabat, A.; Mirjalili, S.; Abd Elaziz, M.; Gandomi, A. H., The arithmetic optimization algorithm, Comput. Methods Appl. Mech. Engrg., 376, Article 113609 pp. (2021) · Zbl 1506.90276
[6] Abualigah, L.; Yousri, D.; Abd Elaziz, M.; Ewees, A. A.; Al-qaness, M. A.; Gandomi, A. H., Aquila optimizer: A novel meta-heuristic optimization algorithm, Comput. Ind. Eng., 157, Article 107250 pp. (2021)
[7] Akyel, T., Some remarks for \(\lambda \)-spirallike function of complex order at the boundary of the unit disc, Commun. Korean Math. Soc., 36, 4, 743-757 (2021) · Zbl 1486.30026
[8] Alcalá-Fdez, J.; Sánchez, L.; Garcia, S.; del Jesus, M. J.; Ventura, S.; Garrell, J. M.; Otero, J.; Romero, C.; Bacardit, J.; Rivas, V. M., Keel: a software tool to assess evolutionary algorithms for data mining problems, Soft Comput., 13, 3, 307-318 (2009)
[9] Askarzadeh, A., A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm, Comput. Struct., 169, 1-12 (2016)
[10] Azerolu, T. A.; Örnek, B., A refined Schwarz inequality on the boundary, Complex Var. Elliptic Equations, 58, 4, 571-577 (2013) · Zbl 1291.30147
[11] Boas, H. P., Julius and julia: Mastering the art of the Schwarz lemma, Amer. Math. Monthly, 117, 9, 770-785 (2010) · Zbl 1205.30021
[12] Cabrera, E.; Taboada, M.; Iglesias, M. L.; Epelde, F.; Luque, E., Simulation optimization for healthcare emergency departments, Procedia Comput. Sci., 9, 1464-1473 (2012)
[13] Çğlar, M.; Orhan, H., \(( \theta , \mu , \tau )\)— Neighborhood for analytic functions involving modified sigmoid function, Commun. Fac. Sci. Univ. Ankara Ser. A1 Math. Statist., 68, 2, 2161-2169 (2019) · Zbl 1494.30021
[14] Chegini, S. N.; Bagheri, A.; Najafi, F., Psoscalf: A new hybrid pso based on sine cosine algorithm and levy flight for solving optimization problems, Appl. Soft Comput., 73, 697-726 (2018)
[15] Chen, Z.; Liu, W., An efficient parameter adaptive support vector regression using k-means clustering and chaotic slime mould algorithm, IEEE Access, 8, 156851-156862 (2020)
[16] Cheng, M.-Y.; Prayogo, D., Symbiotic organisms search: a new metaheuristic optimization algorithm, Comput. Struct., 139, 98-112 (2014)
[17] Chickermane, H.; Gea, H. C., Structural optimization using a new local approximation method, Internat. J. Numer. Methods Engrg., 39, 5, 829-846 (1996) · Zbl 0865.73034
[18] Chou, J.-S.; Truong, D.-N., A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean, Appl. Math. Comput., 389, Article 125535 pp. (2021) · Zbl 1508.90118
[19] Coello, C. A.C., Use of a self-adaptive penalty approach for engineering optimization problems, Comput. Ind., 41, 2, 113-127 (2000)
[20] Črepinšek, M.; Liu, S.-H.; Mernik, M., Exploration and exploitation in evolutionary algorithms: A survey, ACM Comput. Surv., 45, 3, 1-33 (2013) · Zbl 1293.68251
[21] Dhiman, G.; Kumar, V., Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications, Adv. Eng. Softw., 114, 48-70 (2017)
[22] Dubinin, V., The schwarz inequality on the boundary for functions regular in the disk, J. Math. Sci., 122, 6, 3623-3629 (2004) · Zbl 1070.30008
[23] Ekinci, S.; Izci, D.; Zeynelgil, H. L.; Orenc, S., An application of slime mould algorithm for optimizing parameters of power system stabilizer, (2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (2020), IEEE), 1-5
[24] Fogel, L. J.; Owens, A. J.; Walsh, M. J., Artificial Intelligence Through Simulated Evolution (1966), John Wiley & Sons. · Zbl 0148.40701
[25] Gandomi, A. H.; Yang, X.-S.; Alavi, A. H., Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Eng. Comput., 29, 1, 17-35 (2013)
[26] Gao, Z.-M.; Zhao, J.; Li, S.-R., The improved slime mould algorithm with cosine controlling parameters, (Journal of Physics: Conference Series, Vol. 1631 (2020), IOP Publishing), Article 012083 pp.
[27] Gao, Z.-M.; Zhao, J.; Yang, Y.; Tian, X.-J., The hybrid grey wolf optimization-slime mould algorithm, (Journal of Physics: Conference Series, 1617 (2020), IOP Publishing), Article 012034 pp.
[28] Goluzin, G. M., Geometric Theory of Functions of a Complex Variable, Vol. 26 (1969), American Mathematical Soc. · Zbl 0183.07502
[29] Gupta, S.; Deep, K., Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation, Neural Comput. Appl., 32, 13, 9521-9543 (2020)
[30] Gupta, S.; Deep, K.; Moayedi, H.; Foong, L. K.; Assad, A., Sine cosine grey wolf optimizer to solve engineering design problems, Eng. Comput., 1-27 (2020)
[31] 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. Simulation, 192, 84-110 (2022) · Zbl 1540.90296
[32] 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., 51, 3, 1531-1551 (2021)
[33] Hassan, B. A., Cscf: a chaotic sine cosine firefly algorithm for practical application problems, Neural Comput. Appl., 1-20 (2020)
[34] Hatamlou, A., Black hole: A new heuristic optimization approach for data clustering, Inform. Sci., 222, 175-184 (2013)
[35] He, Q.; Wang, L., An effective co-evolutionary particle swarm optimization for constrained engineering design problems, Eng. Appl. Artif. Intell., 20, 1, 89-99 (2007)
[36] He, Q.; Wang, L., A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Appl. Math. Comput., 186, 2, 1407-1422 (2007) · Zbl 1117.65088
[37] Holden, N.; Freitas, A. A., A hybrid pso/aco algorithm for discovering classification rules in data mining, J. Artif. Evol. Appl., 2008 (2008)
[38] Holland, J. H., Genetic algorithms, Sci. Am., 267, 1, 66-73 (1992)
[39] Houssein, E. H.; Hosney, M. E.; Oliva, D.; Mohamed, W. M.; Hassaballah, M., A novel hybrid harris hawks optimization and support vector machines for drug design and discovery, Comput. Chem. Eng., 133, Article 106656 pp. (2020)
[40] Huang, F.-z.; Wang, L.; He, Q., An effective co-evolutionary differential evolution for constrained optimization, Appl. Math. Comput., 186, 1, 340-356 (2007) · Zbl 1114.65061
[41] Hudaib, A. A.; Fakhouri, H. N., Supernova optimizer: a novel natural inspired meta-heuristic, Mod. Appl. Sci., 12, 1, 32-50 (2018)
[42] Issa, M.; Hassanien, A. E.; Oliva, D.; Helmi, A.; Ziedan, I.; Alzohairy, A., Asca-pso: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment, Expert Syst. Appl., 99, 56-70 (2018)
[43] Jack, I., Functions starlike and convex of order \(\alpha \), J. Lond. Math. Soc., 2, 3, 469-474 (1971) · Zbl 0224.30026
[44] Jadon, S. S.; Tiwari, R.; Sharma, H.; Bansal, J. C., Hybrid artificial bee colony algorithm with differential evolution, Appl. Soft Comput., 58, 11-24 (2017)
[45] Karaboga, D.; Basturk, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, J. Global Optim., 39, 3, 459-471 (2007) · Zbl 1149.90186
[46] Karami, H.; Anaraki, M. V.; Farzin, S.; Mirjalili, S., Flow direction algorithm (fda): A novel optimization approach for solving optimization problems, Comput. Ind. Eng., 156, Article 107224 pp. (2021)
[47] Kaveh, A.; Khanzadi, M.; Moghaddam, M. R., Billiards-inspired optimization algorithm; a new meta-heuristic method, (Structures, Vol. 27 (2020), Elsevier), 1722-1739
[48] Kaveh, A.; Talatahari, S., An improved ant colony optimization for constrained engineering design problems, Eng. Comput. (2010) · Zbl 1284.74093
[49] Kaveh, A.; Talatahari, S., A novel heuristic optimization method: charged system search, Acta Mech., 213, 3, 267-289 (2010) · Zbl 1397.65094
[50] Kennedy, J.; Eberhart, R., Particle swarm optimization, (Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4 (1995), IEEE), 1942-1948
[51] Khalilpourazari, S.; Pasandideh, S. H.R., Sine-cosine crow search algorithm: theory and applications, Neural Comput. Appl., 1-18 (2019)
[52] Kirkpatrick, S., Optimization by simulated annealing: Quantitative studies, J. Stat. Phys., 34, 5-6, 975-986 (1984)
[53] Koza, J. R.; Koza, J. R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, Vol. 1 (1992), MIT Press · Zbl 0850.68161
[54] Kumar, C.; Raj, T. D.; Premkumar, M.; Raj, T. D., A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters, Optik, 223, Article 165277 pp. (2020)
[55] Li, S.; Chen, H.; Wang, M.; Heidari, A. A.; Mirjalili, S., Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comput. Syst. (2020)
[56] Li, N.; Wang, L., Bare-bones based sine cosine algorithm for global optimization, J. Comput. Sci., 47, Article 101219 pp. (2020)
[57] Lin, M.-H.; Tsai, J.-F.; Hu, N.-Z.; Chang, S.-C., Design optimization of a speed reducer using deterministic techniques, Math. Probl. Eng., 2013 (2013)
[58] Liu, H.; Cai, Z.; Wang, Y., Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization, Appl. Soft Comput., 10, 2, 629-640 (2010)
[59] Luo, J.; Shi, B., A hybrid whale optimization algorithm based on modified differential evolution for global optimization problems, Appl. Intell., 49, 5, 1982-2000 (2019)
[60] Mahdavi, M.; Fesanghary, M.; Damangir, E., An improved harmony search algorithm for solving optimization problems, Appl. Math. Comput., 188, 2, 1567-1579 (2007) · Zbl 1119.65053
[61] Mateljevic, M., Rigidity of Holomorphic Mappings & Schwarz and Jack Lemma (2018), Press. ResearchGate · Zbl 1391.31002
[62] Mercer, P. R., Boundary schwarz inequalities arising from rogosinski’s lemma, J. Class. Anal., 12, 93-97 (2018) · Zbl 1424.30101
[63] Mercer, P. R., An improved schwarz lemma at the boundary, Open Math., 16, 1, 1140-1144 (2018) · Zbl 1417.30016
[64] Mezura-Montes, E.; Coello, C. A.C., An empirical study about the usefulness of evolution strategies to solve constrained optimization problems, Int. J. Gen. Syst., 37, 4, 443-473 (2008) · Zbl 1219.90129
[65] MiarNaeimi, F.; Azizyan, G.; Rashki, M., Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems, Knowl.-Based Syst., 213, Article 106711 pp. (2021)
[66] Mirjalili, S., Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl.-Based Syst., 89, 228-249 (2015)
[67] Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Comput. Appl., 27, 4, 1053-1073 (2016)
[68] Mirjalili, S., Sca: a sine cosine algorithm for solving optimization problems, Knowl.-Based Syst., 96, 120-133 (2016)
[69] Mirjalili, S.; Gandomi, A. H.; Mirjalili, S. Z.; Saremi, S.; Faris, H.; Mirjalili, S. M., Salp swarm algorithm: A bio-inspired optimizer for engineering design problems, Adv. Eng. Softw., 114, 163-191 (2017)
[70] Mirjalili, S.; Lewis, A., The whale optimization algorithm, Adv. Eng. Softw., 95, 51-67 (2016)
[71] Mirjalili, S.; Mirjalili, S. M.; Hatamlou, A., Multi-verse optimizer: a nature-inspired algorithm for global optimization, Neural Comput. Appl., 27, 2, 495-513 (2016)
[72] Mirjalili, S.; Mirjalili, S. M.; Lewis, A., Grey wolf optimizer, Adv. Eng. Softw., 69, 46-61 (2014)
[73] Moghdani, R.; Abd Elaziz, M.; Mohammadi, D.; Neggaz, N., An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem, Eng. Comput., 1-30 (2020)
[74] Mostafa, M.; Rezk, H.; Aly, M.; Ahmed, E. M., A new strategy based on slime mould algorithm to extract the optimal model parameters of solar pv panel, Sustain. Energy Technol. Assess., 42, Article 100849 pp. (2020)
[75] Naruei, I.; Keynia, F., A new optimization method based on coot bird natural life model, Expert Syst. Appl., Article 115352 pp. (2021)
[76] Naruei, I.; Keynia, F., Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems, Eng. Comput., 1-32 (2021)
[77] Nemati, S.; Basiri, M. E.; Ghasem-Aghaee, N.; Aghdam, M. H., A novel aco-ga hybrid algorithm for feature selection in protein function prediction, Expert Syst. Appl., 36, 10, 12086-12094 (2009)
[78] Nenavath, H.; Jatoth, R. K., Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking, Appl. Soft Comput., 62, 1019-1043 (2018)
[79] Nenavath, H.; Jatoth, R. K., Hybrid sca-tlbo: a novel optimization algorithm for global optimization and visual tracking, Neural Comput. Appl., 31, 9, 5497-5526 (2019)
[80] Onay, F. K.; Aydemir, S. B., Chaotic hunger games search optimization algorithm for global optimization and engineering problems, Math. Comput. Simulation (2021)
[81] Örnek, B. N., Sharpened forms of analytic functions concerned with hankel determinant, Korean J. Math., 27, 4, 1027-1041 (2019) · Zbl 1435.30081
[82] Örnek, B. N.; Düzenli, T., Boundary analysis for the derivative of driving point impedance functions, IEEE Trans. Circuits Syst. II: Express Briefs, 65, 9, 1149-1153 (2018)
[83] Örnek, B. N.; Düzenli, T., On boundary analysis for derivative of driving point impedance functions and its circuit applications, IET Circuits Devices Syst., 13, 2, 145-152 (2019)
[84] Osserman, R., A sharp schwarz inequality on the boundary, Proc. Amer. Math. Soc., 128, 12, 3513-3517 (2000) · Zbl 0963.30014
[85] Pattnaik, P.; Mishra, S.; Mishra, B. S.P., Optimization techniques for intelligent iot applications, Fog Edge Pervas. Comput. Intell. IoT Driven Appl., 311-331 (2020)
[86] Pommerenke, C., Boundary Behaviour of Conformal Maps, Vol. 299 (2013), Springer Science & Business Media
[87] Premalatha, K.; Natarajan, A., Hybrid pso and ga for global maximization, Int. J. Open Probl. Compt. Math., 2, 4, 597-608 (2009) · Zbl 1206.90225
[88] Rashedi, E.; Nezamabadi-Pour, H.; Saryazdi, S., Gsa: a gravitational search algorithm, Inform. Sci., 179, 13, 2232-2248 (2009) · Zbl 1177.90378
[89] Ray, T.; Saini, P., Engineering design optimization using a swarm with an intelligent information sharing among individuals, Eng. Optim., 33, 6, 735-748 (2001)
[90] Rechenberg, I., Evolutionsstrategien, (Simulationsmethoden in der Medizin und Biologie (1978), Springer), 83-114
[91] Rizk-Allah, R. M., Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems, J. Comput. Des. Eng., 5, 2, 249-273 (2018)
[92] Sadollah, A.; Bahreininejad, A.; Eskandar, H.; Hamdi, M., Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Appl. Soft Comput., 13, 5, 2592-2612 (2013)
[93] Sandgren, E., Nonlinear integer and discrete programming in mechanical design optimization (1990)
[94] Saremi, S.; Mirjalili, S.; Lewis, A., Grasshopper optimisation algorithm: theory and application, Adv. Eng. Softw., 105, 30-47 (2017)
[95] Shaheen, M. A.; Hasanien, H. M.; Alkuhayli, A., A novel hybrid gwo-pso optimization technique for optimal reactive power dispatch problem solution, Ain Shams Eng. J. (2020)
[96] Simon, D., Biogeography-based optimization, IEEE Trans. Evol. Comput., 12, 6, 702-713 (2008)
[97] Singh, N.; Chiclana, F.; Magnot, J.-P., A new fusion of salp swarm with sine cosine for optimization of non-linear functions, Eng. Comput., 36, 1, 185-212 (2020)
[98] Singh, N.; Singh, S., A novel hybrid gwo-sca approach for optimization problems, Eng. Sci. Technol. Int. J., 20, 6, 1586-1601 (2017)
[99] Storn, R.; Price, K., Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 4, 341-359 (1997) · Zbl 0888.90135
[100] Tan, K. C.; Chiam, S. C.; Mamun, A.; Goh, C. K., Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization, European J. Oper. Res., 197, 2, 701-713 (2009) · Zbl 1159.90482
[101] Trivedi, I. N.; Jangir, P.; Kumar, A.; Jangir, N.; Totlani, R., A novel hybrid pso-woa algorithm for global numerical functions optimization, (Adv. Comput. Comput. Sci. (2018), Springer), 53-60
[102] Tsai, J.-F., Global optimization of nonlinear fractional programming problems in engineering design, Eng. Optim., 37, 4, 399-409 (2005)
[103] Turgut, O. E., Thermal and economical optimization of a shell and tube evaporator using hybrid backtracking search—sine-cosine algorithm, Arab. J. Sci. Eng., 42, 5, 2105-2123 (2017)
[104] Wolpert, D. H.; Macready, W. G., No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1, 1, 67-82 (1997)
[105] Yang, X.-S., Engineering Optimization: An Introduction with Metaheuristic Applications (2010), John Wiley & Sons
[106] Yang, X.-S.; Deb, S., Cuckoo search via lévy flights, (2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (2009), IEEE), 210-214
[107] Yildirim, A. E.; Karci, A., Application of three bar truss problem among engineering design optimization problems using artificial atom algorithm, (2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (2018), IEEE), 1-5
[108] Zhang, Q.; Li, H., Moea/d: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 6, 712-731 (2007)
[109] Zhang, M.; Luo, W.; Wang, X., Differential evolution with dynamic stochastic selection for constrained optimization, Inform. Sci., 178, 15, 3043-3074 (2008)
[110] Zhang, H.; Sun, J.; Liu, T.; Zhang, K.; Zhang, Q., Balancing exploration and exploitation in multiobjective evolutionary optimization, Inform. Sci., 497, 129-148 (2019) · Zbl 1451.90175
[111] Zhang, J.; Wang, J., Improved salp swarm algorithm based on levy flight and sine cosine operator, IEEE Access, 8, 99740-99771 (2020)
[112] Zhang, J.; Zhou, Y.; Luo, Q., An improved sine cosine water wave optimization algorithm for global optimization, J. Intell. Fuzzy Systems, 34, 4, 2129-2141 (2018)
[113] Zhao, J.; Gao, Z.-M., The hybridized harris hawk optimization and slime mould algorithm, (Journal of Physics: Conference Series, 1682 (2020), IOP Publishing), Article 012029 pp.
[114] Zhao, J.; Gao, Z.-M.; Sun, W., The improved slime mould algorithm with levy flight, (Journal of Physics: Conference Series, Vol. 1617 (2020), IOP Publishing), Article 012033 pp.
[115] Zhao, W.; Wang, L.; Mirjalili, S., Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications, Comput. Methods Appl. Mech. Engrg., 388, Article 114194 pp. (2022) · Zbl 1507.90197
[116] Zhao, F.; Xue, F.; Zhang, Y.; Ma, W.; Zhang, C.; Song, H., A hybrid algorithm based on self-adaptive gravitational search algorithm and differential evolution, Expert Syst. Appl., 113, 515-530 (2018)
[117] Zubaidi, S. L.; Abdulkareem, I. H.; Hashim, K. S.; Al-Bugharbee, H.; Ridha, H. M.; Gharghan, S. K.; Al-Qaim, F. F.; Muradov, M.; Kot, P.; Al-Khaddar, R., Hybridised artificial neural network model with slime mould algorithm: A novel methodology for prediction of urban stochastic water demand, Water, 12, 10, 2692 (2020)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.