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A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem

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Abstract

This paper encompasses a detailed review of state-of-the-art swarm-based algorithms, with a focus on their applications along with a discussion on the merits and limitations of each algorithm. Further, a recently developed advanced particle swarm optimization (APSO) algorithm is compared with the different state-of-the-art algorithms through solving an electromagnetic inverse problem. Results show that the APSO algorithm outperforms the other algorithms. This research provides a scientific guideline for the comparison of different swarm-based algorithms and their utilization regarding specific applications.

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References

  1. Bonabeau, E., Dorigo, M., Theraulaz, G.: From Natural to Artificial Swarm Intelligence. Oxford University Press Inc, Oxford (1999)

    MATH  Google Scholar 

  2. Chakraborty, A., Kar, A.K.: Swarm Intelligence: A Review of Algorithms. In: Patnaik, S., Yang, X.-S., Nakamatsu, K. (eds.) Nature-Inspired Computing and Optimization: Theory and Applications, pp. 475–494. Springer International Publishing, Cham (2017)

    Chapter  Google Scholar 

  3. Li, X., Clerc, M.: Swarm Intelligence. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, pp. 353–384. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  4. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, New York City (2010)

    Google Scholar 

  5. Mitchell, M., Taylor, C.E.: Evolutionary computation: an overview. Annu. Rev. Ecol. Syst. 30(1), 593–616 (1999). https://doi.org/10.1146/annurev.ecolsys.30.1.593

    Article  Google Scholar 

  6. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1975)

    MATH  Google Scholar 

  7. Koza, J.R., Poli, R.: Genetic programming. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 127–164. Springer, Boston (2005)

    Chapter  Google Scholar 

  8. Bäck, T., Hoffmeister, F.: Basic aspects of evolution strategies. Stat. Comput. 4(2), 51–63 (1994). https://doi.org/10.1007/bf00175353

    Article  Google Scholar 

  9. Fogel, D.B.: An overview of evolutionary programming. In: Davis, L.D., De Jong, K., Vose, M.D., Whitley, L.D. (eds.) Evolutionary Algorithms, pp. 89–109. Springer, New York (1999)

    Chapter  Google Scholar 

  10. Eiben, A.E., Smith, J.E.: Evolutionary programming. In: Introduction to Evolutionary Computing, pp. 89–99. Springer, Berlin (2003)

  11. Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. thesis, Politecnico di Milano, Italy. https://ci.nii.ac.jp/naid/10016599043/en/ (1992)

  12. Fan, Y., Wang, G., Lu, X., Wang, G.: Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands. PLoS ONE 14(12), e0226204 (2020). https://doi.org/10.1371/journal.pone.0226204

    Article  Google Scholar 

  13. Dorigo, M., Stützle, T.: Ant colony optimization: overview and recent advances. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, pp. 227–263. Springer, Boston (2010)

    Chapter  Google Scholar 

  14. Hansford, D.: Mob Mentality. No. 123

  15. Yang, Q., et al.: Adaptive multimodal continuous ant colony optimization. IEEE Trans. Evol. Comput. 21(2), 191–205 (2017). https://doi.org/10.1109/TEVC.2016.2591064

    Article  Google Scholar 

  16. Zhang, D., You, X., Liu, S., Yang, K.: Multi-colony ant colony optimization based on generalized Jaccard similarity recommendation strategy. IEEE Access 7, 157303–157317 (2019). https://doi.org/10.1109/ACCESS.2019.2949860

    Article  Google Scholar 

  17. Shang, J., et al.: A review of ant colony optimization based methods for detecting epistatic interactions. IEEE Access 7, 13497–13509 (2019). https://doi.org/10.1109/ACCESS.2019.2894676

    Article  Google Scholar 

  18. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997). https://doi.org/10.1109/4235.585892

    Article  Google Scholar 

  19. Stützle, T., Hoos, H.H.: MAX–MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000). https://doi.org/10.1016/S0167-739X(00)00043-1

    Article  MATH  Google Scholar 

  20. Jian, R., Chen, Y., Chen, T.: Multi-parameters unified-optimization for millimeter wave microstrip antenna based on ICACO. IEEE Access 7, 53012–53017 (2019). https://doi.org/10.1109/ACCESS.2019.2912461

    Article  Google Scholar 

  21. Wang, X., Gu, H., Liu, Y., Zhang, H.: A two-stage RPSO-ACS based protocol: a new method for sensor network clustering and routing in mobile computing. IEEE Access 7, 113141–113150 (2019). https://doi.org/10.1109/ACCESS.2019.2933150

    Article  Google Scholar 

  22. Zhang, H., Wang, X., Memarmoshrefi, P., Hogrefe, D.: A survey of ant colony optimization based routing protocols for mobile ad hoc networks. IEEE Access 5, 24139–24161 (2017). https://doi.org/10.1109/ACCESS.2017.2762472

    Article  Google Scholar 

  23. Wang, H., Wang, Z.A., Yu, L., Wang, X., Liu, C.: Ant colony optimization with improved potential field heuristic for robot path planning. In: 2018 37th Chinese Control Conference (CCC), 25–27 July 2018, pp. 5317–5321 (2018). https://doi.org/10.23919/chicc.2018.8483844

  24. Huang, Y., Gu, Y., Zheng, Z.: Research on the path planning of hair-insertion robot arm based on ant colony optimization. In: 2018 37th Chinese Control Conference (CCC), 25–27 July 2018, pp. 5191–5195. (2018) https://doi.org/10.23919/chicc.2018.8483149

  25. Singh, R., Prasad, L.B.: Optimal trajectory tracking of robotic manipulator using ant colony optimization. In: 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 2–4 Nov 2018, pp. 1–6 (2018). https://doi.org/10.1109/upcon.2018.8597087

  26. Zhu, W., Hou, P., Chang, L., Xu, X.: Disjunctive belief rule base optimization by ant colony optimization for railway transportation safety assessment. In: 2019 Chinese Control and Decision Conference (CCDC), 3–5 June 2019, pp. 6120–6124 (2019). https://doi.org/10.1109/ccdc.2019.8833179

  27. Eaton, J., Yang, S., Gongora, M.: Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling. IEEE Trans. Intell. Transp. Syst. 18(11), 2980–2992 (2017). https://doi.org/10.1109/TITS.2017.2665042

    Article  Google Scholar 

  28. Mavrovouniotis, M., Yang, S., Van, M., Li, C., Polycarpou, M.: Ant colony optimization algorithms for dynamic optimization: a case study of the dynamic travelling salesperson problem [research frontier]. IEEE Comput. Intell. Mag. 15(1), 52–63 (2020). https://doi.org/10.1109/MCI.2019.2954644

    Article  Google Scholar 

  29. Ratanavilisagul, C.: Modified ant colony optimization with pheromone mutation for travelling salesman problem. In: 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 27–30 June 2017, pp. 411–414 (2017). https://doi.org/10.1109/ecticon.2017.8096261

  30. Mavrovouniotis, M., Müller, F.M., Yang, S.: Ant colony optimization with local search for dynamic traveling salesman problems. IEEE Trans. Cybern. 47(7), 1743–1756 (2017). https://doi.org/10.1109/TCYB.2016.2556742

    Article  Google Scholar 

  31. Contreras, R., Pinninghoff, M.A., Ortega, J.: Using ant colony optimization for edge detection in gray scale images. In: Natural and Artificial Models in Computation and Biology, pp. 323–331. Springer, Berlin (2013)

  32. Kaur, S., Kaur, P.: An Edge detection technique with image segmentation using ant colony optimization: a review. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET), 19–19 Nov 2016, pp. 1–5 (2016). https://doi.org/10.1109/get.2016.7916741

  33. Metawa, U.J.N., Shankar, K., Lakshmanaprabu, S.K.: Financial crisis prediction model using ant colony optimization. Int. J. Inf. Manag. 50, 538–556 (2020). https://doi.org/10.1016/j.ijinfomgt.2018.12.001

    Article  Google Scholar 

  34. Marinakis, Y., Marinaki, M., Doumpos, M., Zopounidis, C.: Ant colony and particle swarm optimization for financial classification problems. Expert Syst. Appl. 36(7), 10604–10611 (2009). https://doi.org/10.1016/j.eswa.2009.02.055

    Article  Google Scholar 

  35. Kleinkauf, R., Mann, M., Backofen, R.: antaRNA: ant colony-based RNA sequence design. Bioinformatics 31(19), 3114–3121 (2015). https://doi.org/10.1093/bioinformatics/btv319

    Article  Google Scholar 

  36. Do Duc, D., Dinh, H.Q., Dang, T.H., Laukens, K., Hoang, X.H.: AcoSeeD: an ant colony optimization for finding optimal spaced seeds in biological sequence search. In: Swarm Intelligence, pp. 204–211. Springer, Berlin

  37. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical report-TR06, Technical Report, Erciyes University (2005)

  38. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  39. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009). https://doi.org/10.1016/j.amc.2009.03.090

    Article  MathSciNet  MATH  Google Scholar 

  40. Gao, Y.: An improved hybrid group intelligent algorithm based on artificial bee colony and particle swarm optimization. In: 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), 10–11 Aug 2018, pp. 160–163 (2018). https://doi.org/10.1109/icvris.2018.00046

  41. Wang, B., Wang, L.: A novel artificial bee colony algorithm for numerical function optimization. In: 2012 Fourth International Conference on Computational and Information Sciences, 17–19 Aug 2012, pp. 172–175 (2012). https://doi.org/10.1109/iccis.2012.32

  42. Chengli, F., Qiang, F., Guangzheng, L., Qinghua, X.: Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism. J. Syst. Eng. Electron. 29(2), 405–414 (2018). https://doi.org/10.21629/JSEE.2018.02.20

    Article  Google Scholar 

  43. Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012). https://doi.org/10.1016/j.ins.2010.07.015

    Article  Google Scholar 

  44. Gao, W.-F., Liu, S.-Y.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39(3), 687–697 (2012). https://doi.org/10.1016/j.cor.2011.06.007

    Article  MATH  Google Scholar 

  45. Wang, L., Zhang, X., Zhang, X.: Antenna array design by artificial bee colony algorithm with similarity induced search method. IEEE Trans. Magn. 55(6), 1–4 (2019). https://doi.org/10.1109/TMAG.2019.2896921

    Article  Google Scholar 

  46. Liang, H., Jiang, H.: The modified artificial bee colony-based SLM scheme for PAPR reduction in OFDM systems: In: 2019 International Conference on Artificial Intelligence in Information And Communication (ICAIIC), 11–13 Feb 2019, pp. 504–508 (2019). https://doi.org/10.1109/icaiic.2019.8669020

  47. Salman, A., Qureshi, I.M., Saleem, S., Saeed, S.: Optimization of resource allocation for heterogeneous services in OFDM based cognitive radio networks using artificial bee colony. In: 2019 International Symposium on Recent Advances in Electrical Engineering (RAEE), 28–29 Aug 2019, vol. 4, pp. 1–5 (2019). https://doi.org/10.1109/raee.2019.8886951

  48. Rekaby, A., Youssif, A.A., Eldin, A.S.: Introducing adaptive artificial bee colony algorithm and using it in solving traveling salesman problem. In: 2013 Science and Information Conference, 7–9 Oct 2013, pp. 502–506 (2013)

  49. Wang, Y.: Improving artificial bee colony and particle swarm optimization to solve TSP problem. In: 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), 10–11 Aug 2018, pp. 179–182 (2018). https://doi.org/10.1109/icvris.2018.00051

  50. Kumar, D., Mishra, A., Chatterjee, K.: Power and frequency control of a wind energy power system using artificial bee colony algorithm. In: 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), 23–24 March 2017, pp. 561–565 (2017). https://doi.org/10.1109/iconstem.2017.8261385

  51. Çinar, M., Kaygusuz, A.: Optimum fuel cost in load flow analysis of smart grid by using artificial bee colony algorithm. In: 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 21–22 Sept 2019, pp. 1–5. https://doi.org/10.1109/IDAP.2019.8875893 (2019)

  52. Salehahmadi, Z., Manafi, A.: How can bee colony algorithm serve medicine? World J. Plast. Surg. 3(2), 87–92 (2014)

    Google Scholar 

  53. Gopika, G.S., Shanthini, J., Karthik, S.: Hybrid approach for the brain tumors detection & segmentation using artificial bee colony optimization with FCM. In: 2018 International Conference on Soft-computing and Network Security (ICSNS), 14–16 Feb 2018, pp. 1–5 (2018). https://doi.org/10.1109/icsns.2018.8573648

  54. Keerthika, T.: A Hybrid Fish—Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier (2019)

  55. Farooq, M.U., Salman, Q., Arshad, M., Khan, I., Akhtar, R., Kim, S.: An artificial bee colony algorithm based on a multi-objective framework for supplier integration. Appl. Sci. 9, 588 (2019). https://doi.org/10.3390/app9030588

  56. Xiaoyi, D.: An efficient hybrid artificial bee colony algorithm for customer segmentation in mobile E-commerce. J. Electron. Commer. Organ. (JECO) 11(2), 53–63 (2013). https://doi.org/10.4018/jeco.2013040105

    Article  Google Scholar 

  57. Cuevas, E., Sención-Echauri, F., Zaldivar, D., Pérez, M.: Image segmentation using artificial bee colony optimization. In: Zelinka, I., Snášel, V., Abraham, A. (eds.) Handbook of Optimization: From Classical to Modern Approach, pp. 965–990. Springer, Berlin (2013)

    Chapter  Google Scholar 

  58. Yimit, A., Hagihara, Y., Miyoshi, T., Hagihara, Y.: Automatic image enhancement by artificial bee colony algorithm. In: 2012 International Conference on Graphic and Image Processing. SPIE (2013)

  59. Yang, X., Suash, D.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 9–11 Dec 2009, pp. 210–214 (2009). https://doi.org/10.1109/nabic.2009.5393690

  60. Yang, X.-S., Deb, S.: Multiobjective cuckoo search for design optimization. Comput. Oper. Res. 40(6), 1616–1624 (2013). https://doi.org/10.1016/j.cor.2011.09.026

    Article  MathSciNet  MATH  Google Scholar 

  61. Mareli, M., Twala, B.: An adaptive Cuckoo search algorithm for optimisation. Appl. Comput. Inf. 14(2), 107–115 (2018). https://doi.org/10.1016/j.aci.2017.09.001

    Article  Google Scholar 

  62. Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos, Solitons Fractals 44(9), 710–718 (2011). https://doi.org/10.1016/j.chaos.2011.06.004

    Article  Google Scholar 

  63. Layeb, A., Boussalia, S.R.: A novel quantum inspired cuckoo search algorithm for bin packing problem. Int. J. Inf. Technol. Comput. Sci. 4, 58–67 (2012). https://doi.org/10.5815/ijitcs.2012.05.08

    Article  Google Scholar 

  64. Han, W., Lu, X.S., Zhou, M., Shen, X., Wang, J., Xu, J.: An evaluation and optimization methodology for efficient power plant programs. IEEE Trans. Syst. Man Cybern. Syst. 50(2), 707–716 (2020). https://doi.org/10.1109/TSMC.2017.2714198

    Article  Google Scholar 

  65. Nugraha, D.A., Lian, K.L., Suwarno, : A novel MPPT method based on cuckoo search algorithm and golden section search algorithm for partially shaded PV system. Can. J. Electr. Comput. Eng. 42(3), 173–182 (2019). https://doi.org/10.1109/cjece.2019.2914723

    Article  Google Scholar 

  66. Gupta, G.P.: Improved cuckoo search-based clustering protocol for wireless sensor networks. Procedia Comput. Sci. 125, 234–240 (2018). https://doi.org/10.1016/j.procs.2017.12.032

    Article  Google Scholar 

  67. Goyal, S., Patterh, M.S.: Wireless sensor network localization based on cuckoo search algorithm. Wireless Pers. Commun. 79(1), 223–234 (2014). https://doi.org/10.1007/s11277-014-1850-8

    Article  Google Scholar 

  68. Mohanty, P., Parhi, D.: Optimal path planning for a mobile robot using cuckoo search algorithm. J. Exp. Theor. Artif. Intell. 28, 1–18 (2014). https://doi.org/10.1080/0952813x.2014.971442

    Article  Google Scholar 

  69. Laha, S.: A quantum-inspired cuckoo search algorithm for the travelling salesman problem. In: 2015 International Conference on Computing, Communication and Security (ICCCS), 4–5 Dec 2015, pp. 1–6 (2015). https://doi.org/10.1109/cccs.2015.7374201

  70. Jebril, N.A., Abu Al-Haija, Q.: Cuckoo optimization algorithm (COA) for image processing. In: Hemanth, J., Balas, V.E. (eds.) Nature Inspired Optimization Techniques for Image Processing Applications, pp. 189–213. Springer, Cham (2019)

    Chapter  Google Scholar 

  71. Ashour, A.S., Samanta, S., Dey, N., Kausar, N., Abdessalemkaraa, W.B., Hassanien, A.E.: Computed tomography image enhancement using cuckoo search: a log transform based approach. J. Signal Inf. Process. 06(03), 14 (2015). https://doi.org/10.4236/jsip.2015.63023

    Article  Google Scholar 

  72. Issa, H.H., Ahmed, S.M.E.: FPGA implementation of floating point based cuckoo search algorithm. IEEE Access 7, 134434–134447 (2019). https://doi.org/10.1109/ACCESS.2019.2942205

    Article  Google Scholar 

  73. Krishnanand, K.N., Ghose, D.: Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int. J. Comput. Intell. Stud. 1(1), 93–119 (2009). https://doi.org/10.1504/ijcistudies.2009.025340

    Article  Google Scholar 

  74. Krishnanand, K.N., Ghose, D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell. 3(2), 87–124 (2009). https://doi.org/10.1007/s11721-008-0021-5

    Article  Google Scholar 

  75. Wu, B., Qian, C., Ni, W., Fan, S.: The improvement of glowworm swarm optimization for continuous optimization problems. Expert Syst. Appl. 39(7), 6335–6342 (2012). https://doi.org/10.1016/j.eswa.2011.12.017

    Article  Google Scholar 

  76. Ludwig, S.A.: Improved glowworm swarm optimization algorithm applied to multi-level thresholding. In: 2016 IEEE Congress on Evolutionary Computation (CEC), 24–29 July 2016, pp. 1533–1540 (2016). https://doi.org/10.1109/cec.2016.7743971

  77. Qiong, P., Liao, Y., Hao, P., He, X., Hui, C.: A self-adaptive step glowworm swarm optimization approach. Int. J. Comput. Intell. Appl. 18(01), 1950004 (2019). https://doi.org/10.1142/s1469026819500044

    Article  Google Scholar 

  78. Zheng, X., Gui, Z., Wang, Y.: Support vector machine model based on glowworm swarm optimization in application of vibrant fault diagnosis for hydro-turbine generating unit. In: 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), 3–5 Oct 2017, pp. 238–141 (2017). https://doi.org/10.1109/itoec.2017.8122427

  79. Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., et al.: Multi-spectral satellite image classification using glowworm swarm optimization. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, 24–29 July 2011, pp. 47–50 (2011). https://doi.org/10.1109/igarss.2011.6048894

  80. Zhou, Y.-Q., Ouyang, Z., Liu, J., Sang, G.: A novel K-means image clustering algorithm based on glowworm swarm optimization. Electr. Rev. 88, 266–270 (2012)

    Google Scholar 

  81. Zeng, T., Hua, Y., Zhao, X., Liu, T.: Research on glowworm swarm optimization localization algorithm based on wireless sensor network. In: 2016 IEEE international frequency control symposium (IFCS), 9–12 May 2016, pp. 1–5 (2016). https://doi.org/10.1109/fcs.2016.7546730

  82. Jiang, H., Tang, X.: Polarimetric MIMO radar target detection based on glowworm swarm optimization algorithm. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4–9 May 2014, pp. 805–809 (2014). https://doi.org/10.1109/icassp.2014.6853708

  83. Zhang, Y., Ma, X., Miao, Y.: Localization of multiple odor sources using modified glowworm swarm optimization with collective robots. In: Proceedings of the 30th Chinese Control Conference, 22–24 July 2011, pp. 1899–1904 (2011)

  84. Krishnanand, K.N., Ghose, D.: A glowworm swarm optimization based multi-robot system for signal source localization. In: Liu, D., Wang, L., Tan, K.C. (eds.) Design and Control of Intelligent Robotic Systems, pp. 49–68. Springer, Berlin (2009)

    Chapter  Google Scholar 

  85. Quang, N.N., Sanseverino, E.R., Silvestre, M.L.D., Madonia, A., Li, C., Guerrero, J.M.: Optimal power flow based on glow worm-swarm optimization for three-phase islanded microgrids. In: 2014 AEIT Annual Conference—From Research to Industry: The Need for a More Effective Technology Transfer (AEIT), 18–19 Sept 2014, pp. 1–6 (2014). https://doi.org/10.1109/aeit.2014.7002028

  86. Surender Reddy, S., Srinivasa Rathnam, C.: Optimal power flow using glowworm swarm optimization. Int. J. Electr. Power Energy Syst. 80, 128–139 (2016). https://doi.org/10.1016/j.ijepes.2016.01.036

    Article  Google Scholar 

  87. Wang, X., Yang, K., Zhou, X.: Two-stage glowworm swarm optimisation for economical operation of hydropower station. IET Renew. Power Gener. 12(9), 992–1003 (2018). https://doi.org/10.1049/iet-rpg.2017.0466

    Article  Google Scholar 

  88. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), 16–19 July 2000, vol. 1, pp. 84–88 (2000). https://doi.org/10.1109/cec.2000.870279

  89. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), 4–9 May 1998, pp. 69–73 (1998). https://doi.org/10.1109/icec.1998.699146

  90. Sedarous, S., El-Gokhy, S.M., Sallam, E.: Multi-swarm multi-objective optimization based on a hybrid strategy. Alex. Eng. J. 57(3), 1619–1629 (2018). https://doi.org/10.1016/j.aej.2017.06.017

    Article  Google Scholar 

  91. Lizzi, L., Viani, F., Azaro, R., Massa, A.: Optimization of a spline-shaped UWB antenna by PSO. IEEE Antennas Wirel. Propag. Lett. 6, 182–185 (2007). https://doi.org/10.1109/LAWP.2007.894157

    Article  Google Scholar 

  92. Li, Y., Shao, W., You, L., Wang, B.: An improved PSO algorithm and its application to UWB antenna design. IEEE Antennas Wirel. Propag. Lett. 12, 1236–1239 (2013). https://doi.org/10.1109/LAWP.2013.2283375

    Article  Google Scholar 

  93. Wang, Z., Zhang, T., Kong, L., Cui, G.: Prediction-based PSO algorithm for MIMO radar antenna deployment in dynamic environment. J. Eng. 2019(20), 6646–6650 (2019). https://doi.org/10.1049/joe.2019.0188

    Article  Google Scholar 

  94. Masehian, E., Sedighizadeh, D.: An improved particle swarm optimization method for motion planning of multiple robots. In: Martinoli, A, et al. (eds.) Distributed Autonomous Robotic Systems: The 10th International Symposium, pp. 175–188. Springer, Berlin (2013)

  95. Aziz, N.A.A., Ibrahim, Z.: Asynchronous particle swarm optimization for swarm robotics. Procedia Eng. 41, 951–957 (2012). https://doi.org/10.1016/j.proeng.2012.07.268

    Article  Google Scholar 

  96. Ayari, A., Bouamama, S.: A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization. Robotics Biomim 4(1), 8 (2017). https://doi.org/10.1186/s40638-017-0062-6

    Article  Google Scholar 

  97. Venkatalakshmi, K., Shalinie, S.M.: A customized particle swarm optimization algorithm for image enhancement. In: 2010 international conference on communication control and computing technologies, 7–9 Oct 2010, pp. 603–607 (2010). https://doi.org/10.1109/icccct.2010.5670768

  98. Farshi, T.R., Drake, J.H., Özcan, E.: A multimodal particle swarm optimization-based approach for image segmentation. Expert Syst. Appl. 149, 113233 (2020). https://doi.org/10.1016/j.eswa.2020.113233

    Article  Google Scholar 

  99. Mohsen, F., Hadhoud, M.M., Amin, K.: A new image segmentation method based on particle swarm optimization. Int. Arab Jo. Inf. Technol. 9, 487–493 (2012)

    Google Scholar 

  100. Esmin, A., Lambert-Torres, G.: Application of particle swarm optimization to optimal power system. Int. J. Innov. Comput. Inf. Control 8, 1705–1716 (2013)

    Google Scholar 

  101. Das, T.K., Venayagamoorthy, G.K.: Optimal design of power system stabilizers using a small population based PSO. In: 2006 IEEE Power Engineering Society General Meeting, 18–22 June 2006, p. 7 (2006). https://doi.org/10.1109/pes.2006.1709322

  102. Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., Nakanishi, Y.: A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15(4), 1232–1239 (2000). https://doi.org/10.1109/59.898095

    Article  Google Scholar 

  103. Pandey, P., Soni, S.: Enhance clustering approach using PSO-A* for E-commerce. Int. J. Comput. Appl. 182, 57–60 (2019). https://doi.org/10.5120/ijca2019918405

    Article  Google Scholar 

  104. Yang, W., Xie, Q., Li, M.: Inventory control method of reverse logistics for shipping electronic commerce based on improved multi-objective particle swarm optimization algorithm. J. Coastal Res. 83, 786–790 (2018). https://doi.org/10.2112/si83-128.1

    Article  Google Scholar 

  105. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)

    Chapter  Google Scholar 

  106. Wang, Y., et al.: A novel bat algorithm with multiple strategies coupling for numerical optimization. Mathematics 7(2), 135 (2019)

    Article  Google Scholar 

  107. Swayamsiddha, S., Prateek, S.S., Singh, S.Parija, Pratihar, D.K.: Reporting cell planning-based cellular mobility management using a binary artificial bat algorithm. Heliyon 5(3), e01276 (2019). https://doi.org/10.1016/j.heliyon.2019.e01276

    Article  Google Scholar 

  108. Ng, C.K., Wu, C.H., Ip, W.H., Yung, K.L.: A smart bat algorithm for wireless sensor network deployment in 3-D environment. IEEE Commun. Lett. 22(10), 2120–2123 (2018). https://doi.org/10.1109/LCOMM.2018.2861766

    Article  Google Scholar 

  109. Adarsh, B.R., Raghunathan, T., Jayabarathi, T., Yang, X.-S.: Economic dispatch using chaotic bat algorithm. Energy 96, 666–675 (2016). https://doi.org/10.1016/j.energy.2015.12.096

    Article  Google Scholar 

  110. Biswal, S., Barisal, A.K., Behera, A., Prakash, T.: Optimal power dispatch using BAT algorithm. In: 2013 International Conference on Energy Efficient Technologies for Sustainability, 10–12 April 2013, pp. 1018–1023 (2013). https://doi.org/10.1109/iceets.2013.6533526

  111. Rahmani, M., Ghanbari, A., Ettefagh, M.M.: Robust adaptive control of a bio-inspired robot manipulator using bat algorithm. Expert Syst. Appl. 56, 164–176 (2016). https://doi.org/10.1016/j.eswa.2016.03.006

    Article  Google Scholar 

  112. Rahmani, M., Ghanbari, A., Ettefagh, M.M.: A novel adaptive neural network integral sliding-mode control of a biped robot using bat algorithm. J. Vib. Control 24(10), 2045–2060 (2018). https://doi.org/10.1177/1077546316676734

    Article  MathSciNet  Google Scholar 

  113. Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. SIGGRAPH Comput. Graph. 21(4), 25–34 (1987). https://doi.org/10.1145/37402.37406

    Article  Google Scholar 

  114. Abbass, H.A.: MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), 27–30 May 2001, vol. 1, pp. 207–214 (2001). https://doi.org/10.1109/cec.2001.934391

  115. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002). https://doi.org/10.1109/MCS.2002.1004010

    Article  Google Scholar 

  116. Muller, S.D., Marchetto, J., Airaghi, S., Kournoutsakos, P.: Optimization based on bacterial chemotaxis. IEEE Trans. Evol. Comput. 6(1), 16–29 (2002). https://doi.org/10.1109/4235.985689

    Article  Google Scholar 

  117. Li, X., Shao, Z., Qian, J.I.: An optimizing method based on autonomous animate: fish swarm algorithm. Syst. Eng. Theory Practice 22, 32–38 (2002)

    Google Scholar 

  118. Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resourc. Plan. Manag. 129(3), 210–225 (2003). https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210)

    Article  Google Scholar 

  119. Wedde, H.F., Farooq, M., Zhang, Y.: BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior. In: Ant Colony Optimization and Swarm Intelligence. Springer, Berlin, pp. 83–94

  120. Yang, X.-S.: Engineering optimizations via nature-inspired virtual bee algorithms. In: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Springer, Berlin, pp. 317–323 (2005)

  121. Teodorović, D., Dell’Orco, M.: Bee colony optimization—A cooperative learning approach to complex transportation problems. In: Advanced OR and AI Methods in Transportation, pp. 51–60 (2005)

  122. Li, W.H., et al.: Function optimization method based on bacterial colony chemotaxis. J. Circuits Syst. 10(01), 58–63 (2005)

    Google Scholar 

  123. Drias, H., Sadeg, S., Yahi, S.: Cooperative bees swarm for solving the maximum weighted satisfiability problem. In: Computational Intelligence and Bioinspired Systems. Springer, Berlin, pp. 318–325 (2005)

  124. Haddad, O.B., Afshar, A., Mariño, M.A.: Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour. Manag. 20(5), 661–680 (2006). https://doi.org/10.1007/s11269-005-9001-3

    Article  Google Scholar 

  125. Chu, S.-C., Tsai, P.-W., Pan, J.-S.: Cat swarm optimization. In: PRICAI 2006: Trends in Artificial Intelligence. Springer, Berlin, pp. 854–858 (2006)

  126. Bastos-Filho, C., Lima Neto, F., Lins, A., Nascimento, A., Lima, M.: A novel search algorithm based on fish school behavior, pp. 2646–2651 (2008)

  127. Havens, T.C., Spain, C.J., Salmon, N.G., Keller, J.M.: Roach Infestation Optimization. In: 2008 IEEE Swarm Intelligence Symposium, 21–23 Sept 2008, pp 1–7 (2008). https://doi.org/10.1109/sis.2008.4668317

  128. Ying, C., Hua, M., Huilian, L., Zhen, J., Wu, Q.H.: A fast bacterial swarming algorithm for high-dimensional function optimization. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1–6 June 2008, pp. 3135–3140 (2008). https://doi.org/10.1109/cec.2008.4631222

  129. Padró, F., Navarro, J.: Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour (2009). https://doi.org/10.1145/1543834.1543949

  130. He, S., Wu, Q.H., Saunders, J.R.: Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans. Evol. Comput. 13(5), 973–990 (2009). https://doi.org/10.1109/TEVC.2009.2011992

    Article  Google Scholar 

  131. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Stochastic Algorithms: Foundations and Applications. Springer, Berlin, pp. 169–178 (2009)

  132. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A bumble bees mating optimization algorithm for global unconstrained optimization problems. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 305–318. Springer, Berlin (2010)

    Chapter  Google Scholar 

  133. Zhao Hui, C., Hai Yan, T.: Cockroach swarm optimization. In: Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET’10), vol. 6 (2010). https://doi.org/10.1109/iccet.2010.5485993

  134. Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math Appl. 60(7), 2087–2098 (2010). https://doi.org/10.1016/j.camwa.2010.07.049

    Article  MATH  Google Scholar 

  135. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012). https://doi.org/10.1016/j.cnsns.2012.05.010

    Article  MathSciNet  MATH  Google Scholar 

  136. Tang, R., Fong, S., Yang, X., Deb, S.: Wolf search algorithm with ephemeral memory. In: Seventh International Conference on Digital Information Management (ICDIM 2012), 22–24 Aug 2012, pp. 165–172 (2012). https://doi.org/10.1109/icdim.2012.6360147

  137. Niu, B., Wang, H.: Bacterial colony optimization. Discrete Dyn. Nat. Soc. 2012, 1–29 (2012)

    MathSciNet  MATH  Google Scholar 

  138. B. R. Rajakumar, “The Lion’s Algorithm: A New Nature-Inspired Search Algorithm,” Procedia Technology, vol. 6, pp. 126-135, 2012/01/01/2012, doi: https://doi.org/10.1016/j.protcy.2012.10.016

  139. Taherdangkoo, M.: A novel meta-heuristic algorithm for numerical function optimization: blind, naked mole-rats (BNMR) algorithm. Sci. Res. Essays 7(41), 3566–3583 (2012)

    Article  Google Scholar 

  140. Pan, W.-T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl. Based Syst. 26, 69–74 (2012). https://doi.org/10.1016/j.knosys.2011.07.001

    Article  Google Scholar 

  141. Cuevas, E., Cienfuegos, M., Zaldívar, D., Pérez-Cisneros, M.: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 40(16), 6374–6384 (2013). https://doi.org/10.1016/j.eswa.2013.05.041

    Article  Google Scholar 

  142. Eesa, A., Mohsin Abdulazeez, A., Orman, Z.: A New Tool for Global Optimization Problems-Cuttlefish Algorithm (2014)

  143. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2014/03/01/2014, doi: https://doi.org/10.1016/j.advengsoft.2013.12.007

  144. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014). https://doi.org/10.1007/s12293-013-0128-0

    Article  Google Scholar 

  145. Li, X., Zhang, J., Yin, M.: Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput. Appl. 24(7), 1867–1877 (2014). https://doi.org/10.1007/s00521-013-1433-8

    Article  Google Scholar 

  146. Wang, G.-G., Deb, S., Cui, Z.: Monarch butterfly optimization. Neural Comput. Appl. 31(7), 1995–2014 (2019). https://doi.org/10.1007/s00521-015-1923-y

    Article  Google Scholar 

  147. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015). https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  148. Wang, G.-G., Deb, S., Coelho, L.: Elephant Herding Optimization (2015)

  149. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016). https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  150. Wu, T.-Q., Yao, M., Yang, J.-H.: Dolphin swarm algorithm. Front. Inf. Technol. Electron. Eng. 17(8), 717–729 (2016). https://doi.org/10.1631/fitee.1500287

    Article  Google Scholar 

  151. Topal, A.O., Altun, O.: A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Inf. Sci. 354, 222–235 (2016). https://doi.org/10.1016/j.ins.2016.03.025

    Article  Google Scholar 

  152. 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). https://doi.org/10.1007/s00521-015-1920-1

    Article  MathSciNet  Google Scholar 

  153. Chen, Y., Peng, B.: Multi-objective optimization on multi-layer configuration of cathode electrode for polymer electrolyte fuel cells via computational-intelligence-aided design and engineering framework. Appl. Soft Comput. 43, 357–371 (2016). https://doi.org/10.1016/j.asoc.2016.02.045

    Article  Google Scholar 

  154. Chen, Y., Wang, Z., Yang, E., Li, Y.: Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm. In: 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), 15–17 Dec 2016, pp. 116–121. (2016). https://doi.org/10.1109/skima.2016.7916207

  155. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  156. Dhiman, G., Kumar, V.: Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70 (2017). https://doi.org/10.1016/j.advengsoft.2017.05.014

    Article  Google Scholar 

  157. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017). https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  158. 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). https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  159. Shamsaldin, A.S., Rashid, T.A., Al-Rashid Agha, R.A., Al-Salihi, N.K., Mohammadi, M.: Donkey and smuggler optimization algorithm: a collaborative working approach to path finding. J. Comput. Des. Eng. 6(4), 562–583 (2019). https://doi.org/10.1016/j.jcde.2019.04.004

    Article  Google Scholar 

  160. Abdullah, J.M., Ahmed, T.: Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7, 43473–43486 (2019). https://doi.org/10.1109/ACCESS.2019.2907012

    Article  Google Scholar 

  161. Khan, T.A., Ling, S.H., Mohan, A.S.: Advanced particle swarm optimization algorithm with improved velocity update strategy. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 7–10 Oct 2018, pp. 3944–3949 (2018). https://doi.org/10.1109/smc.2018.00669

  162. Coco, S., Laudani, A., Riganti Fulginei, F., Salvini, A.: TEAM problem 22 approached by a hybrid artificial life method. COMPEL Int. J. Comput. Mat. Electr. Electr. Eng. 31(3), 816–826 (2012). https://doi.org/10.1108/03321641211209726

    Article  Google Scholar 

  163. Rehman, O.U., Rehman, S.U., Tu, S., Khan, S., Waqas, M., Yang, S.: A quantum particle swarm optimization method with fitness selection methodology for electromagnetic inverse problems. IEEE Access 6, 63155–63163 (2018). https://doi.org/10.1109/ACCESS.2018.2873670

    Article  Google Scholar 

  164. Guimaraes, F.G., Campelo, F., Saldanha, R.R., Igarashi, H., Takahashi, R.H.C., Ramirez, J.A.: A multiobjective proposal for the TEAM benchmark problem 22. IEEE Trans. Magn. 42(4), 1471–1474 (2006). https://doi.org/10.1109/TMAG.2006.871570

    Article  Google Scholar 

  165. Khan, S.U., Yang, S., Wang, L., Liu, L.: A modified particle swarm optimization algorithm for global optimizations of inverse problems. IEEE Trans. Magn. 52(3), 1–4 (2016). https://doi.org/10.1109/TMAG.2015.2487678

    Article  Google Scholar 

  166. Alotto, P., et al.: SMES optimization benchmark extended: introducing Pareto optimal solutions into TEAM22. IEEE Trans. Magn. 44(6), 1066–1069 (2008). https://doi.org/10.1109/TMAG.2007.916091

    Article  Google Scholar 

  167. Karban, P., Kropík, P., Kotlan, V., Doležel, I.: Bayes approach to solving T.E.A.M. benchmark problems 22 and 25 and its comparison with other optimization techniques. Appl. Math. Comput. 319, 681–692 (2018). https://doi.org/10.1016/j.amc.2017.07.043

    Article  MathSciNet  MATH  Google Scholar 

  168. Coelho, L., Alotto, P.: Global optimization of electromagnetic devices using an exponential quantum-behaved particle swarm optimizer. IEEE Trans. Magn. 44, 1074–1077 (2008). https://doi.org/10.1109/tmag.2007.916032

    Article  Google Scholar 

  169. Alotto, U.B.P.G., Freschi, F., Jaindl, M., et al.: Repetto: TEAM Workshop Problem 22: SMES Optimization Benchmark

  170. Duan, Q., Shao, C., Li, X., Shi, Y.: Visualizing the Search Dynamics in a High-Dimensional Space for a Particle Swarm Optimizer, pp. 994–1002 (2017)

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    Talha Ali Khan & Sai Ho Ling

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Khan, T.A., Ling, S.H. A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem. J Comput Electron 19, 1606–1628 (2020). https://doi.org/10.1007/s10825-020-01567-6

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