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Review Article

Circulatory System Based Optimization (CSBO): an expert multilevel biologically inspired meta-heuristic algorithm

, , , ORCID Icon, , , , , & ORCID Icon show all
Pages 1483-1525 | Received 09 Jul 2021, Accepted 03 Jul 2022, Published online: 21 Jul 2022

References

  • Abdechiri, M., Meybodi, M. R., & Bahrami, H. (2013). Gases Brownian motion optimization: An algorithm for optimization (GBMO). Applied Soft Computing, 13(5), 2932–2946. https://doi.org/10.1016/j.asoc.2012.03.068
  • Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887–5958. https://doi.org/10.1002/int.22535
  • Abdullah, J. M., & Ahmed, T. (2019). Fitness dependent optimizer: Inspired by the bee swarming reproductive process. IEEE Access, 7, 43473–43486. https://doi.org/10.1109/ACCESS.2019.2907012
  • Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. A. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8–22. https://doi.org/10.1016/j.swevo.2015.07.002
  • Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250. https://doi.org/10.1016/j.cie.2021.107250
  • Afonso, L. D., Mariani, V. C., & dos Santos Coelho, L. (2013). Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization. Expert Systems with Applications, 40(9), 3794–3802. https://doi.org/10.1016/j.eswa.2012.12.093
  • Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131–159. https://doi.org/10.1016/j.ins.2020.06.037
  • Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. https://doi.org/10.1016/j.eswa.2021.115079
  • Ahmadianfar, I., Heidari, A. A., Noshadian, S., Chen, H., & Gandomi, A. H. (2022). INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 116516. https://doi.org/10.1016/j.eswa.2022.116516
  • Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001–1014. https://doi.org/10.1007/s10845-010-0393-4
  • Akyol, S., & Alatas, B. (2017). Plant intelligence based metaheuristic optimization algorithms. Artificial Intelligence Review, 47(4), 417–462. https://doi.org/10.1007/s10462-016-9486-6
  • Alatas, B. (2011). ACROA: Artificial chemical reaction optimization algorithm for global optimization. Expert Systems with Applications, 38(10), 13170–13180. https://doi.org/10.1016/j.eswa.2011.04.126
  • Alsalibi, B., Abualigah, L., & Khader, A. T. (2021). A novel bat algorithm with dynamic membrane structure for optimization problems. Applied Intelligence, 51(4), 1992–2017. https://doi.org/10.1007/s10489-020-01898-8
  • Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2020). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53(3), 2237–2264. https://doi.org/10.1007/s10462-019-09732-5
  • Arora, J. (2004). Introduction to optimum design. Elsevier.
  • Askari, Q., Younas, I., & Saeed, M. (2020). Political optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems, 195, 105709. https://doi.org/10.1016/j.knosys.2020.105709
  • Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 169, 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
  • Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661–4667). IEEE. https://doi.org/10.1109/CEC.2007.4425083
  • Aydilek, I. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232–249. https://doi.org/10.1016/j.asoc.2018.02.025
  • Baykasoğlu, A., & Akpinar, Ş. (2017). Weighted superposition attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 1: Unconstrained optimization. Applied Soft Computing, 56, 520–540. https://doi.org/10.1016/j.asoc.2015.10.036
  • Bayraktar, Z., Komurcu, M., Bossard, J. A., & Werner, D. H. (2013). The wind driven optimization technique and its application in electromagnetics. IEEE Transactions on Antennas and Propagation, 61(5), 2745–2757. https://doi.org/10.1109/TAP.2013.2238654
  • Becerra, R. L., & Coello, C. A. C. (2006). Cultured differential evolution for constrained optimization. Computer Methods in Applied Mechanics and Engineering, 195(33–36), 4303–4322. https://doi.org/10.1016/j.cma.2005.09.006
  • Braik, M. S. (2021). Chameleon swarm algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Systems with Applications, 174, 114685. https://doi.org/10.1016/j.eswa.2021.114685
  • Brajevic, I. (2015). Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Computing and Applications, 26(7), 1587–1601. https://doi.org/10.1007/s00521-015-1826-y
  • Brajevic, I., & Tuba, M. (2013). An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. Journal of Intelligent Manufacturing, 24(4), 729–740. https://doi.org/10.1007/s10845-011-0621-6
  • Brajevic, I., Tuba, M., & Subotic, M. (2011). Performance of the improved artificial bee colony algorithm on standard engineering constrained problems. International Journal of Mathematics And Computers in Simulation, 5(2), 135–143.
  • Chen, T.-C. (2006). IAs based approach for reliability redundancy allocation problems. Applied Mathematics and Computation, 182(2), 1556–1567. https://doi.org/10.1016/j.amc.2006.05.044
  • Chen, X., Tianfield, H., Mei, C., Du, W., & Liu, G. (2017). Biogeography-based learning particle swarm optimization. Soft Computing, 21(24), 7519–7541. https://doi.org/10.1007/s00500-016-2307-7
  • Chen, X., Xu, B., Yu, K., & Du, W. (2018). Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering. Journal of Applied Mathematics, 2018, 1–19. https://doi.org/10.1155/2018/1806947
  • Cheng, L., Wu, X., & Wang, Y. (2018). Artificial flora (AF) optimization algorithm. Applied Sciences, 8(3), 329. https://doi.org/10.3390/app8030329
  • Cheng, R., & Jin, Y. (2015). A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences, 291, 43–60. https://doi.org/10.1016/j.ins.2014.08.039
  • 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. https://doi.org/10.1016/j.engappai.2018.04.021
  • Coello, C. A. C., & Montes, E. M. (2002). Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, 16(3), 193–203. https://doi.org/10.1016/S1474-0346(02)00011-3
  • Dai, C., Hu, Z., Li, Z., Xiong, Z., & Su, Q. (2020). An improved grey prediction evolution algorithm based on topological opposition-based learning. IEEE Access, 8, 30745–30762. https://doi.org/10.1109/ACCESS.2020.2973197
  • Das, S., & Suganthan, P. N. (2010). Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technological University, Kolkata, 341–359.
  • de Melo, V. V., & Carosio, G. L. C. (2012). Evaluating differential evolution with penalty function to solve constrained engineering problems. Expert Systems with Applications, 39(9), 7860–7863. https://doi.org/10.1016/j.eswa.2012.01.123
  • Dhadwal, M. K., Jung, S. N., & Kim, C. J. (2014). Advanced particle swarm assisted genetic algorithm for constrained optimization problems. Computational Optimization and Applications, 58(3), 781–806. https://doi.org/10.1007/s10589-014-9637-0
  • Dhiman, G., Garg, M., Nagar, A., Kumar, V., & Dehghani, M. (2021). A novel algorithm for global optimization: Rat swarm optimizer. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8457–8482. https://doi.org/10.1007/s12652-020-02580-0
  • Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50. https://doi.org/10.1016/j.knosys.2018.06.001
  • Dorigo, M., & di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470–1477). IEEE.
  • dos Santos Coelho, L. (2010). Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications, 37(2), 1676–1683. https://doi.org/10.1016/j.eswa.2009.06.044
  • Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. MHS’95. In Proceedings of the sixth international symposium on micro machine and human science (pp. 39–43). IEEE.
  • Elsisi, M. (2019). Future search algorithm for optimization. Evolutionary Intelligence, 12(1), 21–31. https://doi.org/10.1007/s12065-018-0172-2
  • Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110-111, 151–166. https://doi.org/10.1016/j.compstruc.2012.07.010
  • Fan, Y., Jiang, W., Zou, Y., Li, J., Chen, J., & Deng, X. (2009). Numerical simulation of pulsatile non-Newtonian flow in the carotid artery bifurcation. Acta Mechanica Sinica, 25(2), 249–255. https://doi.org/10.1007/s10409-009-0227-9
  • Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191, 105190. https://doi.org/10.1016/j.knosys.2019.105190
  • Feng, Z., Niu, W., & Liu, S. (2021). Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Applied Soft Computing, 98, 106734. https://doi.org/10.1016/j.asoc.2020.106734
  • Gablonsky, J. M., & Kelley, C. T. (2001). A locally-biased form of the DIRECT algorithm. Journal of Global Optimization, 21(1), 27–37. https://doi.org/10.1023/A:1017930332101
  • 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. https://doi.org/10.1007/s00366-011-0241-y
  • Gandomi, A. H., Yang, X.-S., Alavi, A. H., & Talatahari, S. (2013). Bat algorithm for constrained optimization tasks. Neural Computing and Applications, 22(6), 1239–1255. https://doi.org/10.1007/s00521-012-1028-9
  • Gao, W.-F., Yen, G. G., & Liu, S.-Y. (2014). A dual-population differential evolution with coevolution for constrained optimization. IEEE Transactions on Cybernetics, 45(5), 1108–1121. https://doi.org/10.1109/TCYB.2014.2345478
  • GhaemiDizaji, M., Dadkhah, C., & Leung, H. (2020). OHDA: An opposition based high dimensional optimization algorithm. Applied Soft Computing, 91, 106185. https://doi.org/10.1016/j.asoc.2020.106185
  • Ghafil, H. N., & Jármai, K. (2020). Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications. Applied Soft Computing, 93, 106392. https://doi.org/10.1016/j.asoc.2020.106392
  • Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: A simple and efficient variant of PSO. Soft Computing, 23(19), 9701–9718. https://doi.org/10.1007/s00500-018-3536-8
  • Ghasemi, M., Davoudkhani, I. F., Akbari, E., Rahimnejad, A., Ghavidel, S., & Li, L. (2020). A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent flow of water-based optimization (TFWO). Engineering Applications of Artificial Intelligence, 92, 103666. https://doi.org/10.1016/j.engappai.2020.103666
  • Ghasemi, M., Ghavidel, S., Aghaei, J., Akbari, E., & Li, L. (2018). CFA optimizer: A new and powerful algorithm inspired by Franklin’s and Coulomb’s laws theory for solving the economic load dispatch problems. International Transactions on Electrical Energy Systems, 28(5), e2536. https://doi.org/10.1002/etep.2536
  • Ghasemi, M., Rahimnejad, A., Hemmati, R., Akbari, E., & Gadsden, S. A. (2021). Wild geese algorithm: A novel algorithm for large scale optimization based on the natural life and death of wild geese. Array, 11, 100074. https://doi.org/10.1016/j.array.2021.100074
  • Ghasemi, M., Taghizadeh, M., Ghavidel, S., & Abbasian, A. (2016). Colonial competitive differential evolution: An experimental study for optimal economic load dispatch. Applied Soft Computing, 40, 342–363. https://doi.org/10.1016/j.asoc.2015.11.033
  • Ghavidel, S., Azizivahed, A., & Li, L. (2018). A hybrid Jaya algorithm for reliability–redundancy allocation problems. Engineering Optimization, 50(4), 698–715. https://doi.org/10.1080/0305215X.2017.1337755
  • Ghosh, A., Chowdhury, A., Sinha, S., Vasilakos, A. V., & Das, S. (2012). A genetic Lbest Particle Swarm Optimizer with dynamically varying subswarm topology. In A. Hussein, E. Daryl, & S. Ruhul (Eds.), 2012 IEEE congress on evolutionary computation (pp. 1–7). IEEE.
  • Gong, W., Cai, Z., & Liang, D. (2014). Engineering optimization by means of an improved constrained differential evolution. Computer Methods in Applied Mechanics and Engineering, 268, 884–904. https://doi.org/10.1016/j.cma.2013.10.019
  • Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications, 119, 210–230. https://doi.org/10.1016/j.eswa.2018.10.050
  • Gupta, S., Deep, K., & Engelbrecht, A. P. (2020). A memory guided sine cosine algorithm for global optimization. Engineering Applications of Artificial Intelligence, 93, 103718. https://doi.org/10.1016/j.engappai.2020.103718
  • Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646–667. https://doi.org/10.1016/j.future.2019.07.015
  • Hayyolalam, V., & Kazem, A. A. P. (2020). Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249. https://doi.org/10.1016/j.engappai.2019.103249
  • He, Q., Hu, X., Ren, H., & Zhang, H. (2015). A novel artificial fish swarm algorithm for solving large-scale reliability–redundancy application problem. ISA Transactions, 59, 105–113. https://doi.org/10.1016/j.isatra.2015.09.015
  • He, Q., & Wang, L. (2007a). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation, 186(2), 1407–1422. https://doi.org/10.1016/j.amc.2006.07.134
  • He, Q., & Wang, L. (2007b). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1), 89–99. https://doi.org/10.1016/j.engappai.2006.03.003
  • Heidari, A. A., Ali Abbaspour, R., & Rezaee Jordehi, A. (2017). An efficient chaotic water cycle algorithm for optimization tasks. Neural Computing and Applications, 28(1), 57–85. https://doi.org/10.1007/s00521-015-2037-2
  • 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. https://doi.org/10.1016/j.future.2019.02.028
  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–72. https://doi.org/10.1038/scientificamerican0792-66
  • Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731. https://doi.org/10.1016/j.engappai.2020.103731
  • pixabay.com. (n.d.).
  • Hu, J., Gui, W., Heidari, A. A., Cai, Z., Liang, G., Chen, H., & Pan, Z. (2022). Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection. Knowledge-Based Systems, 237, 107761. https://doi.org/10.1016/j.knosys.2021.107761
  • Huang, C.-L. (2015). A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems. Reliability Engineering & System Safety, 142, 221–230. https://doi.org/10.1016/j.ress.2015.06.002
  • Huang, F., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 186(1), 340–356. https://doi.org/10.1016/j.amc.2006.07.105
  • Huang, G. (2016). Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm. Swarm and Evolutionary Computation, 27, 31–67. https://doi.org/10.1016/j.swevo.2015.09.007
  • Hudaib, A. A., & Fakhouri, H. N. (2018). Supernova optimizer: A novel natural inspired meta-heuristic. Modern Applied Science, 12(1), 32–50. https://doi.org/10.5539/mas.v12n1p32
  • Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Lévy flight. Expert Systems with Applications, 165, 113902. https://doi.org/10.1016/j.eswa.2020.113902
  • Izci, D., Ekinci, S., Eker, E., & Kayri, M. (2022). Augmented hunger games search algorithm using logarithmic spiral opposition-based learning for function optimization and controller design. Journal of King Saud University-Engineering Sciences. https://doi.org/10.1016/j.jksues.2022.03.001
  • Jaddi, N. S., Alvankarian, J., & Abdullah, S. (2017). Kidney-inspired algorithm for optimization problems. Communications in Nonlinear Science and Numerical Simulation, 42, 358–369. https://doi.org/10.1016/j.cnsns.2016.06.006
  • Jaderyan, M., & Khotanlou, H. (2016). Virulence optimization algorithm. Applied Soft Computing, 43, 596–618. https://doi.org/10.1016/j.asoc.2016.02.038
  • Jahani, E., & Chizari, M. (2018). Tackling global optimization problems with a novel algorithm – Mouth brooding fish algorithm. Applied Soft Computing, 62, 987–1002. https://doi.org/10.1016/j.asoc.2017.09.035
  • Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44, 148–175. https://doi.org/10.1016/j.swevo.2018.02.013
  • Johnston, B. M., Johnston, P. R., Corney, S., & Kilpatrick, D. (2006). Non-Newtonian blood flow in human right coronary arteries: Transient simulations. Journal of Biomechanics, 39(6), 1116–1128. https://doi.org/10.1016/j.jbiomech.2005.01.034
  • Kaboli, S. H. A., Selvaraj, J., & Rahim, N. A. (2017). Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems. Journal of Computational Science, 19, 31–42. https://doi.org/10.1016/j.jocs.2016.12.010
  • Kallioras, N. A., Lagaros, N. D., & Avtzis, D. N. (2018). Pity beetle algorithm – A new metaheuristic inspired by the behavior of bark beetles. Advances in Engineering Software, 121, 147–166. https://doi.org/10.1016/j.advengsoft.2018.04.007
  • Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. https://doi.org/10.1016/j.engappai.2020.103541
  • Kaveh, A., & Bakhshpoori, T. (2016). Water evaporation optimization: A novel physically inspired optimization algorithm. Computers & Structures, 167, 69–85. https://doi.org/10.1016/j.compstruc.2016.01.008
  • Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110, 69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014
  • Kaveh, A., & Eslamlou, A. D. (2020). Water strider algorithm: A new metaheuristic and applications. Structures, 25, 520–541. https://doi.org/10.1016/j.istruc.2020.03.033
  • Khishe, M., & Mosavi, M. R. (2020). Chimp optimization algorithm. Expert Systems with Applications, 149, 113338. https://doi.org/10.1016/j.eswa.2020.113338
  • Kirkpatrick, S., Gelatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671
  • Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194(36–38), 3902–3933. https://doi.org/10.1016/j.cma.2004.09.007
  • Lei, Z., Gao, S., Gupta, S., Cheng, J., & Yang, G. (2020). An aggregative learning gravitational search algorithm with self-adaptive gravitational constants. Expert Systems with Applications, 152, 113396. https://doi.org/10.1016/j.eswa.2020.113396
  • Li, C., Yang, S., & Nguyen, T. T. (2011). A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(3), 627–646. https://doi.org/10.1109/TSMCB.2011.2171946
  • Li, G., Niu, P., & Xiao, X. (2012). Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Applied Soft Computing, 12(1), 320–332. https://doi.org/10.1016/j.asoc.2011.08.040
  • Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055
  • Li, W., & Wang, G. G. (2021). Improved elephant herding optimization using opposition-based learning and K-means clustering to solve numerical optimization problems. Journal of Ambient Intelligence and Humanized Computing, 1–32. https://doi.org/10.1007/s12652-021-03391-7
  • Liang, J.-J., & Suganthan, P. N. (2005). Dynamic multi-swarm particle swarm optimizer. In Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005 (pp. 124–129). IEEE.
  • Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295. https://doi.org/10.1109/TEVC.2005.857610
  • 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 numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635, 490.
  • Liao, T. W. (2010). Two hybrid differential evolution algorithms for engineering design optimization. Applied Soft Computing, 10(4), 1188–1199. https://doi.org/10.1016/j.asoc.2010.05.007
  • Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629–640. https://doi.org/10.1016/j.asoc.2009.08.031
  • Maia, R. D., de Castro, L. N., & Caminhas, W. M. (2014). Real-parameter optimization with optbees. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 2649–2655). IEEE.
  • Maity, K., & Mishra, H. (2018). ANN modelling and elitist teaching learning approach for multi-objective optimization of $$μ$$ μ-EDM. Journal of Intelligent Manufacturing, 29(7), 1599–1616. https://doi.org/10.1007/s10845-016-1193-2
  • Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1(4), 355–366. https://doi.org/10.1016/j.ecoinf.2006.07.003
  • Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation, 8(3), 204–210. https://doi.org/10.1109/TEVC.2004.826074
  • Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022
  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Moghdani, R., & Salimifard, K. (2018). Volleyball premier league algorithm. Applied Soft Computing, 64, 161–185. https://doi.org/10.1016/j.asoc.2017.11.043
  • Mohamed, A.-A. A., Hassan, S. A., Hemeida, A. M., Alkhalaf, S., Mahmoud, M. M. M., & Eldin, A. M. B. (2020). Parasitism – predation algorithm (PPA): A novel approach for feature selection. Ain Shams Engineering Journal, 11(2), 293–308. https://doi.org/10.1016/j.asej.2019.10.004
  • Mohamed, A. W., & Sabry, H. Z. (2012). Constrained optimization based on modified differential evolution algorithm. Information Sciences, 194, 171–208. https://doi.org/10.1016/j.ins.2012.01.008
  • Muthiah-Nakarajan, V., & Noel, M. M. (2016). Galactic swarm optimization: A new global optimization metaheuristic inspired by galactic motion. Applied Soft Computing, 38, 771–787. https://doi.org/10.1016/j.asoc.2015.10.034
  • Naruei, I., & Keynia, F. (2021). A new optimization method based on COOT bird natural life model. Expert Systems with Applications, 183, 115352. https://doi.org/10.1016/j.eswa.2021.115352
  • Parsopoulos, K. E., & Vrahatis, M. N. (2005). Unified particle swarm optimization for solving constrained engineering optimization problems. In L. Wang, K. Chen, & Y. S. Ong (Eds.), International conference on natural computation (pp. 582–591). Springer.
  • Pasandideh, S. H. R., Niaki, S. T. A., & Hajipour, V. (2013). A multi-objective facility location model with batch arrivals: Two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing, 24(2), 331–348. https://doi.org/10.1007/s10845-011-0592-7
  • Patel, V. K., & Savsani, V. J. (2015). Heat transfer search (HTS): A novel optimization algorithm. Information Sciences, 324, 217–246. https://doi.org/10.1016/j.ins.2015.06.044
  • Połap, D., & Woźniak, M. (2021). Red fox optimization algorithm. Expert Systems with Applications, 166, 114107. https://doi.org/10.1016/j.eswa.2020.114107
  • Preux, P., Munos, R., & Valko, M. (2014). Bandits attack function optimization. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 2245–2252). IEEE.
  • Punnathanam, V., & Kotecha, P. (2016). Yin-Yang-pair optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 54, 62–79. https://doi.org/10.1016/j.engappai.2016.04.004
  • Qiao, Z., Shan, W., Jiang, N., Heidari, A. A., Chen, H., Teng, Y., & Mafarja, M. (2021). Gaussian bare-bones gradient-based optimization: Towards mitigating the performance concerns. International Journal of Intelligent Systems, 37, 3193–3254. https://doi.org/10.1002/int.22658
  • Radosavljević, J. (2018). Metaheuristic optimization in power engineering. Institution of Engineering and Technology.
  • Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19–34. https://doi.org/10.5267/j.ijiec.2015.8.004
  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015
  • Rao, R. V., & Waghmare, G. G. (2017). A new optimization algorithm for solving complex constrained design optimization problems. Engineering Optimization, 49(1), 60–83. https://doi.org/10.1080/0305215X.2016.1164855
  • Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
  • Ray, T., & Liew, K.-M. (2003). Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation, 7(4), 386–396. https://doi.org/10.1109/TEVC.2003.814902
  • Rechenberg, I. (1989). Evolution strategy: Nature’s way of optimization. In H. W. Bergmann (Ed.), Optimization: Methods and applications, possibilities and limitations (pp. 106–126). Springer.
  • Sadollah, A., Sayyaadi, H., & Yadav, A. (2018). A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing, 71, 747–782. https://doi.org/10.1016/j.asoc.2018.07.039
  • Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 105, 30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004
  • Satapathy, S. C., & Naik, A. (2014). Modified teaching–learning-based optimization algorithm for global numerical optimization—A comparative study. Swarm and Evolutionary Computation, 16, 28–37. https://doi.org/10.1016/j.swevo.2013.12.005
  • Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20–34. https://doi.org/10.1016/j.engappai.2019.01.001
  • Sharma, T. K., Kumar Sahoo, A., & Goyal, P. (2021). Bidirectional butterfly optimization algorithm and engineering applications. Materials Today: Proceedings, 34, 736–741. https://doi.org/10.1016/j.matpr.2020.04.679
  • Sheikhalishahi, M., Ebrahimipour, V., Shiri, H., Zaman, H., & Jeihoonian, M. (2013). A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem. The International Journal of Advanced Manufacturing Technology, 68(1–4), 317–338. https://doi.org/10.1007/s00170-013-4730-6
  • Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004
  • Singh, A., & Sundar, S. (2011). An artificial bee colony algorithm for the minimum routing cost spanning tree problem. Soft Computing, 15(12), 2489–2499. https://doi.org/10.1007/s00500-011-0711-6
  • Singh, P. R., Abd Elaziz, M., & Xiong, S. (2019). Ludo game-based metaheuristics for global and engineering optimization. Applied Soft Computing, 84, 105723. https://doi.org/10.1016/j.asoc.2019.105723
  • Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Daniyal, H. (2020). Barnacles mating optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103330. https://doi.org/10.1016/j.engappai.2019.103330
  • Sun, C., Zeng, J., & Pan, J. (2011). An improved vector particle swarm optimization for constrained optimization problems. Information Sciences, 181(6), 1153–1163. https://doi.org/10.1016/j.ins.2010.11.033
  • Tang, D., Dong, S., Jiang, Y., Li, H., & Huang, Y. (2015). ITGO: Invasive tumor growth optimization algorithm. Applied Soft Computing, 36, 670–698. https://doi.org/10.1016/j.asoc.2015.07.045
  • Tsai, H.-C. (2014). Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Information Sciences, 258, 80–93. https://doi.org/10.1016/j.ins.2013.09.015
  • Tu, J., Chen, H., Wang, M., & Gandomi, A. H. (2021). The colony predation algorithm. Journal of Bionic Engineering, 18(3), 674–710. https://doi.org/10.1007/s42235-021-0050-y
  • Vommi, V. B., & Vemula, R. (2018). A very optimistic method of minimization (VOMMI) for unconstrained problems. Information Sciences, 454-455, 255–274. https://doi.org/10.1016/j.ins.2018.04.046
  • Wang, G. G. (2018). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151–164. https://doi.org/10.1007/s12293-016-0212-3
  • Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In S. Deb, L. W. Santoso, & S. Fong (Eds.), 2015 3rd international symposium on computational and business intelligence (ISCBI) (pp. 1–5). IEEE.
  • 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. https://doi.org/10.1504/IJBIC.2018.093328
  • Wang, G. G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/s00521-015-1923-y
  • Wang, L., & Li, L. (2010). An effective differential evolution with level comparison for constrained engineering design. Structural and Multidisciplinary Optimization, 41(6), 947–963. https://doi.org/10.1007/s00158-009-0454-5
  • Wang, L., Yang, B., & Chen, Y. (2014). Improving particle swarm optimization using multi-layer searching strategy. Information Sciences, 274, 70–94. https://doi.org/10.1016/j.ins.2014.02.143
  • Wang, L., Yang, B., & Orchard, J. (2016). Particle swarm optimization using dynamic tournament topology. Applied Soft Computing, 48, 584–596. https://doi.org/10.1016/j.asoc.2016.07.041
  • Wang, Y., Cai, Z., & Zhang, Q. (2011). Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, 15(1), 55–66. https://doi.org/10.1109/TEVC.2010.2087271
  • Wang, Y., Cai, Z., Zhou, Y., & Fan, Z. (2009). Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Structural and Multidisciplinary Optimization, 37(4), 395–413. https://doi.org/10.1007/s00158-008-0238-3
  • Wei, B., Xia, X., Yu, F., Zhang, Y., Xu, X., Wu, H., & He, G. (2020). Multiple adaptive strategies based particle swarm optimization algorithm. Swarm and Evolutionary Computation, 57, 100731. https://doi.org/10.1016/j.swevo.2020.100731
  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
  • Wu, G., Mallipeddi, R., & Suganthan, P. N. (2017). Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore. Technical Report.
  • Wu, Z.-S., Fu, W.-P., & Xue, R. (2015). Nonlinear inertia weighted teaching-learning-based optimization for solving global optimization problem. Computational Intelligence and Neuroscience, 2015, 1–15. https://doi.org/10.1155/2015/292576
  • Xia, X., Gui, L., He, G., Wei, B., Zhang, Y., Yu, F., Wu, H., & Zhan, Z.-H. (2020). An expanded particle swarm optimization based on multi-exemplar and forgetting ability. Information Sciences, 508, 105–120. https://doi.org/10.1016/j.ins.2019.08.065
  • Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. https://doi.org/10.1016/j.eswa.2021.114864
  • Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545–568. https://doi.org/10.1016/j.asoc.2019.03.012
  • Yashesh, D., Deb, K., & Bandaru, S. (2014). Non-uniform mapping in real-coded genetic algorithms. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 2237–2244). IEEE.
  • Yazdani, M., & Jolai, F. (2016). Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, 3(1), 24–36. https://doi.org/10.1016/j.jcde.2015.06.003
  • Yildiz, A. R. (2013a). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13(3), 1561–1566. https://doi.org/10.1016/j.asoc.2011.12.016
  • Yildiz, A. R. (2013b). Optimization of multi-pass turning operations using hybrid teaching learning-based approach. The International Journal of Advanced Manufacturing Technology, 66(9), 1319–1326. https://doi.org/10.1007/s00170-012-4410-y
  • Yu, K., Wang, X., & Wang, Z. (2016). An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing, 27(4), 831–843. https://doi.org/10.1007/s10845-014-0918-3
  • Zaldívar, D., Morales, B., Rodríguez, A., Valdivia-G, A., Cuevas, E., & Pérez-Cisneros, M. (2018). A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior. Biosystems, 174, 1–21. https://doi.org/10.1016/j.biosystems.2018.09.007
  • Zhang, G., Cheng, J., Gheorghe, M., & Meng, Q. (2013). A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing, 13(3), 1528–1542. https://doi.org/10.1016/j.asoc.2012.05.032
  • Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464–490. https://doi.org/10.1016/j.apm.2018.06.036
  • Zhang, L., Oh, S.-K., Pedrycz, W., Yang, B., & Wang, L. (2021). A promotive particle swarm optimizer with double hierarchical structures. IEEE Transactions on Cybernetics, 1–15. https://doi.org/10.1109/TCYB.2021.3101880
  • Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074. https://doi.org/10.1016/j.ins.2008.02.014
  • Zhang, Q., Wang, R., Yang, J., Ding, K., Li, Y., & Hu, J. (2017). Collective decision optimization algorithm: A new heuristic optimization method. Neurocomputing, 221, 123–137. https://doi.org/10.1016/j.neucom.2016.09.068
  • Zhao, W., Wang, L., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194. https://doi.org/10.1016/j.cma.2021.114194
  • Zhao, W., Wang, L., & Zhang, Z. (2019). A novel atom search optimization for dispersion coefficient estimation in groundwater. Future Generation Computer Systems, 91, 601–610. https://doi.org/10.1016/j.future.2018.05.037
  • Zhao, W., Wang, L., & Zhang, Z. (2020). Artificial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 32(13), 9383–9425. https://doi.org/10.1007/s00521-019-04452-x
  • Zhou, Y., Wang, Y., Chen, X., Zhang, L., & Wu, K. (2017). A novel path planning algorithm based on plant growth mechanism. Soft Computing, 21(2), 435–445. https://doi.org/10.1007/s00500-016-2045-x
  • Zou, D., Gao, L., Li, S., & Wu, J. (2011). An effective global harmony search algorithm for reliability problems. Expert Systems with Applications, 38(4), 4642–4648. https://doi.org/10.1016/j.eswa.2010.09.120
  • Zou, F., Chen, D., Lu, R., & Wang, P. (2017). Hierarchical multi-swarm cooperative teaching–learning-based optimization for global optimization. Soft Computing, 21(23), 6983–7004. https://doi.org/10.1007/s00500-016-2237-4