A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 10
Oct.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
J. Tang, G. Liu, and Q. T. Pan, "A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1627-1643, Oct. 2021. doi: 10.1109/JAS.2021.1004129
Citation: J. Tang, G. Liu, and Q. T. Pan, "A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1627-1643, Oct. 2021. doi: 10.1109/JAS.2021.1004129

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

doi: 10.1109/JAS.2021.1004129
Funds:  This work was supported in part by the National Natural Science Foundation of China (62073330), in part by the Natural Science Foundation of Hunan Province (2019JJ20021, 2020JJ4339) and in part by the Scientific Research Fund of Hunan Province Education Department (20B272)
More Information
  • Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

     

  • loading
  • [1]
    M. Rubenstein, A. Cornejo, and R. Nagpal, “Programmable self-assembly in a thousand-robot swarm,” Science, vol. 345, no. 6198, pp. 795–799, Aug. 2014. doi: 10.1126/science.1254295
    [2]
    J. H. De Villiers, “Cape Bees and “animal intelligence”,” Nature, vol. 28, no. 705, pp. 5–6, May 1883.
    [3]
    T. Sasaki and D. Biro, “Cumulative culture can emerge from collective intelligence in animal groups,” Nat. Commun., vol. 8, no. 1, Article No. 15049, Apr. 2017. doi: 10.1038/ncomms15049
    [4]
    A. Chakraborty and A. K. Kar, “Swarm intelligence: A review of algorithms,” in Nature-Inspired Computing and Optimization: Theory and Applications, S. Patnaik, X. S. Yang, and K. Nakamatsu, Eds. Cham, Germany: Springer, 2017, pp. 475−494.
    [5]
    G. Beni and J. Wang, “Swarm intelligence in cellular robotic systems,” in Robots and Biological Systems: Towards a New Bionics?, P. Dario, G. Sandini, and P. Aebischer, Eds. Berlin, Heidelberg, Germany: Springer, 1993, pp. 703−712.
    [6]
    J. X. Chen, “The evolution of computing: AlphaGo,” Comput. Sci. Eng., vol. 18, no. 4, pp. 4–7, Jul.–Aug. 2016. doi: 10.1109/MCSE.2016.74
    [7]
    J. G. Puckett, D. H. Kelley, and N. T. Ouellette, “Searching for effective forces in laboratory insect swarms,” Sci. Rep., vol. 4, p. 4766, Apr. 2014.
    [8]
    Y. Tan and K. Ding, “A survey on GPU-based implementation of swarm intelligence algorithms,” IEEE Trans. Cybern., vol. 46, no. 9, pp. 2028–2041, Sept. 2016. doi: 10.1109/TCYB.2015.2460261
    [9]
    M. Dorigo, “Optimization, learning and natural algorithms,” Ph.D. dissertation, Politecnico di Milano, Milano, Italy, 1992.
    [10]
    C. Blum and M. Dorigo, “The hyper-cube framework for ant colony optimization,” IEEE Trans. Syst.,Man,Cybern.,Part B (Cybern.), vol. 34, no. 2, pp. 1161–1172, Apr. 2004. doi: 10.1109/TSMCB.2003.821450
    [11]
    M. Dorigo and C. Blum, “Ant colony optimization theory: A survey,” Theor. Comput. Sci., vol. 344, no. 2–3, pp. 243–278, Nov. 2015.
    [12]
    K. M. Sim and W. H. Sun, “Ant colony optimization for routing and load-balancing: Survey and new directions,” IEEE Trans. Syst.,Man,Cybern.-Part A:Syst. Hum., vol. 33, no. 5, pp. 560–572, Sept. 2003. doi: 10.1109/TSMCA.2003.817391
    [13]
    M. Dorigo and G. Di Caro, “Ant colony optimization: A new meta-heuristic,” in Proc. Congr. Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, USA, 1999, pp. 1470−1477.
    [14]
    H. M. Botee and E. Bonabeau, “Evolving ant colony optimization,” Adv. Complex Syst., vol. 1, no. 2–3, pp. 149–159, Jun.–Sept. 1998.
    [15]
    T. Stützle and H. H. Hoos, “MAX–MIN ant system,” Future Generat. Comput. Syst., vol. 16, no. 8, pp. 889–914, Jun. 2000. doi: 10.1016/S0167-739X(00)00043-1
    [16]
    B. Bullnheimer, R. F. Hartl, and C. Strauss, “A new rank based version of the ant system–A computational study,” Cen. Eur. J. Oper. Res., vol. 7, no. 1, pp. 25–38, Jan. 1999.
    [17]
    X. M. Hu, J. Zhang, and Y. Li, “Orthogonal methods based ant colony search for solving continuous optimization problems,” J. Comput. Sci. Technol., vol. 23, no. 1, pp. 2–18, Jan. 2008. doi: 10.1007/s11390-008-9111-5
    [18]
    D. K. Gupta, Y. Arora, U. K. Singh, and J. P. Gupta, “Recursive ant colony optimization for estimation of parameters of a function,” in Proc. 1st Int. Conf. Recent Advances in Information Technology, Dhanbad, India, 2012, pp. 448−454.
    [19]
    S. C. Gao, Y. R. Wang, J. J. Cheng, Y. Inazumi, and Z. Tang, “Ant colony optimization with clustering for solving the dynamic location routing problem,” Appl. Math. Comput., vol. 285, pp. 149–173, Jul. 2016.
    [20]
    H. Hemmatian, A. Fereidoon, A. Sadollah, and A. Bahreininejad, “Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization,” Adv. Eng. Softw., vol. 57, pp. 8–18, Mar. 2013. doi: 10.1016/j.advengsoft.2012.11.005
    [21]
    J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. ICNN’95–Int. Conf. Neural Networks, Perth, Australia, 1995, pp. 1942−1948.
    [22]
    D. B. Fogel and H. G. Beyer, “A note on the empirical evaluation of intermediate recombination,” Evol. Comput., vol. 3, no. 4, pp. 491–495, Dec. 1995. doi: 10.1162/evco.1995.3.4.491
    [23]
    Y. H. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” in Proc. 7th Int. Conf. Evolutionary Programming, San Diego, California, USA, 1998, pp. 591−600.
    [24]
    M. Clerc, Particle Swarm Optimization. Hoboken, USA: John Wiley & Sons, 2010.
    [25]
    Z. G. Ren, T. H. Chen, and Z. Z. Wu, “Optimal matching control of a low energy charged particle beam in particle accelerators,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 460–470, Mar. 2019. doi: 10.1109/JAS.2018.7511270
    [26]
    R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intell., vol. 1, no. 1, pp. 33–57, Aug. 2007. doi: 10.1007/s11721-007-0002-0
    [27]
    Y. Gao, W. B. Du, and G. Yan, “Selectively-informed particle swarm optimization,” Sci. Rep., vol. 5, p. 9295, Mar. 2015.
    [28]
    P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 309–313, Feb. 2015. doi: 10.1109/LGRS.2014.2337320
    [29]
    Y. H. Li, Z. H. Zhan, S. J. Lin, J. Zhang, and X. N. Luo, “Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems,” Inf. Sci., vol. 293, pp. 370–382, Feb. 2015. doi: 10.1016/j.ins.2014.09.030
    [30]
    J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281–295, Jun. 2006. doi: 10.1109/TEVC.2005.857610
    [31]
    S. Cui and D. S. Weile, “Application of a parallel particle swarm optimization scheme to the design of electromagnetic absorbers,” IEEE Trans. Antennas Propag., vol. 53, no. 11, pp. 3616–3624, Nov. 2005. doi: 10.1109/TAP.2005.858866
    [32]
    J. C. Zeng and Z. H. Cui, “A guaranteed global convergence particle swarm optimizer,” J. Comput. Res. Dev., vol. 41, no. 8, pp. 1333–1338, Aug. 2004.
    [33]
    M. Clerc, “Discrete particle swarm optimization, illustrated by the traveling salesman problem,” in New Optimization Techniques in Engineering, G. C. Onwubolu and B. V. Babu, Eds. Berlin, Heidelberg, Germany: Springer, 2004, pp. 219−239.
    [34]
    H. W. Lin, B. Zhao, D. R. Liu, and C. Alippi, “Data-based fault tolerant control for affine nonlinear systems through particle swarm optimized neural networks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954–964, Jul. 2020. doi: 10.1109/JAS.2020.1003225
    [35]
    P. Roy, G. S. Mahapatra, and K. N. Dey, “Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1365–1383, Nov. 2019. doi: 10.1109/JAS.2019.1911753
    [36]
    Z. M. Lv, L. Q. Wang, Z. Y. Han, J. Zhao, and W. Wang, “Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 838–849, May 2019. doi: 10.1109/JAS.2019.1911450
    [37]
    X. L. Li, Z. J. Shao, and J. X. Qian, “An optimizing method based on autonomous animats: Fish-swarm algorithm,” Syst. Eng.-Theory Pract., vol. 22, no. 11, pp. 32–38, Nov. 2002.
    [38]
    W. Shen, X. P. Guo, C. Wu, and D. S. Wu, “Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm,” Knowl.-Based Syst., vol. 24, no. 3, pp. 378–385, Apr. 2011. doi: 10.1016/j.knosys.2010.11.001
    [39]
    M. Neshat, G. Sepidnam, M. Sargolzaei, and A. N. Toosi, “Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications,” Artif. Intell. Rev., vol. 42, no. 4, pp. 965–997, May 2014. doi: 10.1007/s10462-012-9342-2
    [40]
    X. L. Li and J. X. Qian, “Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques,” J. Circuits Syst., vol. 8, no. 1, pp. 1–6, Feb. 2002.
    [41]
    C. R. Wang, C. L. Zhou, and J. W. Ma, “An improved artificial fish-swarm algorithm and its application in feed-forward neural networks,” in Proc. Int. Conf. Machine Learning and Cybernetics, Guangzhou, China, 2005, pp. 2890−2894.
    [42]
    X. W. Wang, N. Gao, S. X. Cai, and M. Huang, “An artificial fish swarm algorithm based and ABC supported QoS unicast routing scheme in NGI,” in Proc. Int. Conf. Frontiers of High Performance Computing and Networking, Sorrento, Italy, 2006, pp. 205−214.
    [43]
    A. K. Azad, A. M. A. Rocha, and E. M. G. P. Fernandes, “Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems,” Swarm Evol. Comput., vol. 14, pp. 66–75, Feb. 2014. doi: 10.1016/j.swevo.2013.09.002
    [44]
    Z. Q. Zhang, K. P. Wang, L. X. Zhu, and Y. Wang, “A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem,” Expert Syst. Appl., vol. 86, pp. 165–176, Nov. 2017. doi: 10.1016/j.eswa.2017.05.053
    [45]
    K. C. Zhu and M. Y. Jiang, “Quantum artificial fish swarm algorithm,” in Proc. 8th World Congr. on Intelligent Control and Automation, Jinan, China, 2010, pp. 1−5.
    [46]
    X. Z. Gao, Y. Wu, K. Zenger, and X. L. Huang, “A knowledge-based artificial fish-swarm algorithm,” in Proc. 13th IEEE Int. Conf. Computational Science and Engineering, Hong Kong, China, 2010, pp. 327−332.
    [47]
    D. Yazdani, A. N. Toosi, and M. R. Meybodi, “Fuzzy adaptive artificial fish swarm algorithm,” in Proc. 23rd Australasian Joint Conf. Artificial Intelligence, Adelaide, Australia, 2010, pp. 334–343.
    [48]
    K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst. Mag., vol. 22, no. 3, pp. 52–67, Jun. 2002. doi: 10.1109/MCS.2002.1004010
    [49]
    S. Das, A. Biswas, S. Dasgupta, and A. Abraham, “Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications,” in Foundations of Computational Intelligence Volume 3, A. Abraham, A. E. Hassanien, P. Siarry, and A. Engelbrecht, Eds. Berlin, Heidelberg, Germany: Springer, 2009, pp. 23−55.
    [50]
    E. S. Ali and S. M. Abd-Elazim, “Bacteria foraging optimization algorithm based load frequency controller for interconnected power system,” Int. J. Electr. Power Energy Syst., vol. 33, pp. 633–638, 2011. doi: 10.1016/j.ijepes.2010.12.022
    [51]
    K. R. Devabalaji and K. Ravi, “Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm,” Ain Shams Eng. J., vol. 7, no. 3, pp. 959–971, Sept. 2016. doi: 10.1016/j.asej.2015.07.002
    [52]
    R. Majhi, G. Panda, B. Majhi, and G. Sahoo, “Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques,” Expert Syst. Appl., vol. 36, no. 6, pp. 10097–10104, Aug. 2009. doi: 10.1016/j.eswa.2009.01.012
    [53]
    B. Niu, H. Wang, J. W. Wang, and L. J. Tan, “Multi-objective bacterial foraging optimization,” Neurocomputing, vol. 116, pp. 336–345, Sept. 2013. doi: 10.1016/j.neucom.2012.01.044
    [54]
    H. N. Chen, Y. L. Zhu, and K. Y. Hu, “Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning,” Appl. Soft Comput., vol. 10, no. 2, pp. 539–547, Mar. 2010. doi: 10.1016/j.asoc.2009.08.023
    [55]
    S. V. R. S. Gollapudi, S. S. Pattnaik, O. P. Bajpai, S. Devi, and K. M. Bakwad, “Velocity modulated bacterial foraging optimization technique (VMBFO),” Appl. Soft Comput., vol. 11, no. 1, pp. 154–165, Jan. 2011. doi: 10.1016/j.asoc.2009.11.006
    [56]
    D. H. Kim, A. Abraham, and J. H. Cho, “A hybrid genetic algorithm and bacterial foraging approach for global optimization,” Inf. Sci., vol. 177, no. 18, pp. 3918–3937, Sept. 2007. doi: 10.1016/j.ins.2007.04.002
    [57]
    D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Erciyes Univ., Kayseri, Turkey, Tech. Rep. TR06, 2005.
    [58]
    D. Karaboga and B. Akay, “A comparative study of artificial bee colony algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, Aug. 2009.
    [59]
    J. C. Bansal, H. Sharma, and S. S. Jadon, “Artificial bee colony algorithm: A survey,” Int. J. Adv. Intell. Paradig., vol. 5, no. 1–2, pp. 123–159, 2013.
    [60]
    B. Akay and D. Karaboga, “A survey on the applications of artificial bee colony in signal, image, and video processing,” Signal,Image Video Process., vol. 9, no. 4, pp. 967–990, Mar. 2015. doi: 10.1007/s11760-015-0758-4
    [61]
    A. Kaur and S. Goyal, “A survey on the applications of bee colony optimization techniques,” Int. J. Comput. Sci. Eng., vol. 3, no. 8, pp. 3037–3046, Aug. 2011.
    [62]
    W. F. Gao and S. Y. Liu, “A modified artificial bee colony algorithm,” Comput. Oper. Res., vol. 39, no. 3, pp. 687–697, Mar. 2012. doi: 10.1016/j.cor.2011.06.007
    [63]
    A. Banharnsakun, T. Achalakul, and B. Sirinaovakul, “The best-so-far selection in artificial bee colony algorithm,” Appl. Soft Comput., vol. 11, no. 2, pp. 2888–2901, Mar. 2011. doi: 10.1016/j.asoc.2010.11.025
    [64]
    P. W. Tsai, J. S. Pan, B. Y. Liao, and S. C. Chu, “Enhanced artificial bee colony optimization,” Int. J. Innovative Comput.,Inf. Control, vol. 5, no. 12, pp. 5081–5092, Dec. 2009.
    [65]
    C. Ozturk, E. Hancer, and D. Karaboga, “Dynamic clustering with improved binary artificial bee colony algorithm,” Appl. Soft Comput., vol. 28, pp. 69–80, Mar. 2015. doi: 10.1016/j.asoc.2014.11.040
    [66]
    G. P. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function optimization,” Appl. Math. Comput., vol. 217, no. 7, pp. 3166–3173, Dec. 2010.
    [67]
    Q. K. Pan, M. F. Tasgetiren, and P. N. Suganthan, and T. J. Chua, “A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem,” Inf. Sci., vol. 181, no. 12, pp. 2455–2468, Jun. 2011. doi: 10.1016/j.ins.2009.12.025
    [68]
    M. S. Kımathran and O. Fımathndımathk, “A directed artificial bee colony algorithm,” Appl. Soft Comput., vol. 26, pp. 454–462, Jan. 2015. doi: 10.1016/j.asoc.2014.10.020
    [69]
    D. Karaboga and B. Gorkemli, “A combinatorial artificial bee colony algorithm for traveling salesman problem,” in Proc. Int. Symp. Innovations in Intelligent Systems and Applications, Istanbul, Turkey, 2011, pp. 50−53.
    [70]
    J. K. Ji, S. B. Song, C. Tang, S. C. Gao, Z. Tang, and Y. Todo, “An artificial bee colony algorithm search guided by scale-free networks,” Inf. Sci., vol. 473, pp. 142–165, Jan. 2019. doi: 10.1016/j.ins.2018.09.034
    [71]
    D. D. Zhang, G. M. Xie, J. Z. Yu, and L. Wang, “Adaptive task assignment for multiple mobile robots via swarm intelligence approach,” Rob. Auton. Syst., vol. 55, no. 7, pp. 572–588, Jul. 2007. doi: 10.1016/j.robot.2007.01.008
    [72]
    J. Timmis, P. Andrews, and E. Hart, “On artificial immune systems and swarm intelligence,” Swarm Intell., vol. 4, no. 4, pp. 247–273, Sept. 2010. doi: 10.1007/s11721-010-0045-5
    [73]
    S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp swarm algorithm: A bio-inspired optimizer for engineering design problems,” Adv. Eng. Softw., vol. 114, pp. 163–191, Dec. 2017. doi: 10.1016/j.advengsoft.2017.07.002
    [74]
    K. Z. Gao, Z. G. Cao, L. Zhang, Z. H. Chen, Y. Y. Han, and Q. K. Pan, “A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 904–916, Jul. 2019. doi: 10.1109/JAS.2019.1911540
    [75]
    X. S. Yang, S. Deb, Y. X. Zhao, S. Fong, and X. S. He, “Swarm intelligence: Past, present and future,” Soft Comput., vol. 22, no. 18, pp. 5923–5933, Sept. 2018. doi: 10.1007/s00500-017-2810-5
    [76]
    A. Slowik and H. Kwasnicka, “Nature inspired methods and their industry applications—swarm intelligence algorithms,” IEEE Trans. Ind. Inform., vol. 14, no. 3, pp. 1004–1015, Mar. 2018. doi: 10.1109/TII.2017.2786782
    [77]
    X. S. Yang and S. Deb, “Cuckoo search via Láevy flights,” in Proc. World Congr. Nature & Biologically Inspired Computing, Coimbatore, India, 2009, pp. 210−214.
    [78]
    H. B. Duan and P. X. Qiao, “Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning,” Int. J. Intell. Comput. Cybern., vol. 7, no. 1, pp. 24–37, Mar. 2014. doi: 10.1108/IJICC-02-2014-0005
    [79]
    X. S. Yang and A. H. Gandomi, “Bat algorithm: A novel approach for global engineering optimization,” Eng. Comput., vol. 29, no. 5, pp. 464–483, Jul. 2012. doi: 10.1108/02644401211235834
    [80]
    S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, Mar. 2014. doi: 10.1016/j.advengsoft.2013.12.007
    [81]
    S. A. Hofmeyr and S. Forrest, “Architecture for an artificial immune system,” Evol. Comput., vol. 8, no. 4, pp. 443–473, Dec. 2000. doi: 10.1162/106365600568257
    [82]
    W. T. Pan, “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example,” Knowl.-Based Syst., vol. 26, pp. 69–74, Feb. 2012. doi: 10.1016/j.knosys.2011.07.001
    [83]
    K. N. Krishnanand and D. Ghose, “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions,” Swarm Intell., vol. 3, no. 2, pp. 87–124, Jun. 2009. doi: 10.1007/s11721-008-0021-5
    [84]
    A. R. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization,” Ecol. Inform., vol. 1, no. 4, pp. 355–366, Dec. 2006. doi: 10.1016/j.ecoinf.2006.07.003
    [85]
    Y. R. Wang, Y. Yu, S. Y. Cao, X. Y. Zhang, and S. C. Gao, “A review of applications of artificial intelligent algorithms in wind farms,” Artif. Intell. Rev., vol. 53, no. 5, pp. 3447–3500, Jun. 2020. doi: 10.1007/s10462-019-09768-7
    [86]
    M. L. Pinedo, Scheduling. New York, USA: Springer, 2012.
    [87]
    P. B. Myszkowski, M. E. Skowroński, L. P. Olech, and K. Oślizło, “Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem,” Soft Comput., vol. 19, no. 12, pp. 3599–3619, Dec. 2015. doi: 10.1007/s00500-014-1455-x
    [88]
    J. García-Nieto, E. Alba, and A. C. Olivera, “Swarm intelligence for traffic light scheduling: Application to real urban areas,” Eng. Appl. Artif. Intell., vol. 25, no. 2, pp. 274–283, Mar. 2012. doi: 10.1016/j.engappai.2011.04.011
    [89]
    Q. K. Pan, “An effective co-evolutionary artificial bee colony algorithm for steelmaking–continuous casting scheduling,” Eur. J. Oper. Res., vol. 250, no. 3, pp. 702–714, May 2016. doi: 10.1016/j.ejor.2015.10.007
    [90]
    K. P. Kumar, B. Saravanan, and K. S. Swarup, “Optimization of renewable energy sources in a microgrid using artificial fish swarm algorithm,” Energy Proced., vol. 90, pp. 107–113, Dec. 2016. doi: 10.1016/j.egypro.2016.11.175
    [91]
    A. B. Bamini and S. Enoch, “Optimized resource scheduling using classification and regression tree and modified bacterial foraging optimization algorithm,” Int. J. Appl. Eng. Res., vol. 10, no. 16, pp. 37170–37175, Sept. 2015.
    [92]
    J. Kennedy and R. Eberhart, “Swarm intelligence in cellular robotic systems,” in Proc. IEEE Int. Conf. Neural Networks, Piscataway, USA, 1995, pp. 1942−1948.
    [93]
    G. Z. Tan, H. He, and A. Sloman, “Ant colony system algorithm for real-time globally optimal path planning of mobile robots,” Acta Autom. Sinica, vol. 33, no. 3, pp. 279–285, Mar. 2007. doi: 10.1360/aas-007-0279
    [94]
    M. A. Contreras-Cruz, V. Ayala-Ramirez, and U. H. Hernandez-Belmonte, “Mobile robot path planning using artificial bee colony and evolutionary programming,” Appl. Soft Comput., vol. 30, pp. 319–328, May 2015. doi: 10.1016/j.asoc.2015.01.067
    [95]
    Z. H. Yao and Z. H. Ren, “Path planning for coalmine rescue robot based on hybrid adaptive artificial fish swarm algorithm,” Int. J. Control Autom., vol. 7, no. 8, pp. 1–12, Aug. 2014. doi: 10.14257/ijca.2014.7.8.01
    [96]
    J. Pugh, A. Martinoli, and Y. Zhang, “Particle swarm optimization for unsupervised robotic learning,” in Proc. IEEE Swarm Intelligence Symp., Pasadena, USA, 2005, pp. 92−99.
    [97]
    M. Aghajarian, K. Kiani, and M. M. Fateh, “Design of fuzzy controller for robot manipulators using bacterial foraging optimization algorithm,” J. Intell. Learn. Syst. Appl., vol. 4, no. 1, pp. 53–58, Feb. 2012.
    [98]
    H. Bai and B. Zhao, “A survey on application of swarm intelligence computation to electric power system,” in Proc. 6th World Congr. Intelligent Control and Automation, Dalian, China, 2006, pp. 7587−7591.
    [99]
    J. Z. Zhou, C. Wang, Y. Z. Li, P. Wang, C. L. Li, P. Lu, and L. Mo, “A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security,” Appl. Math. Model., vol. 45, pp. 684–704, May 2017. doi: 10.1016/j.apm.2017.01.001
    [100]
    M. A. Abido, “Optimal design of power-system stabilizers using particle swarm optimization,” IEEE Trans. Energy Conver., vol. 17, no. 3, pp. 406–413, Sept. 2002. doi: 10.1109/TEC.2002.801992
    [101]
    H. Gozde, M. C. Taplamacioglu, and I. Kocaarslan, “Comparative performance analysis of artificial bee colony algorithm in automatic generation control for interconnected reheat thermal power system,” Int. J. Electr. Power Energy Syst., vol. 42, pp. 167–178, 2012. doi: 10.1016/j.ijepes.2012.03.039
    [102]
    C. Li and S. L. Wang, “Next-day power market clearing price forecasting using artificial fish-swarm based neural network,” in Proc. 3rd Int. Symp. Neural Networks, Chengdu, China, 2006, pp. 1290−1295.
    [103]
    E. S. Ali and S. M. Abd-Elazim, “BFOA based design of PID controller for two area load frequency control with nonlinearities,” Int. J. Electr. Power Energy Syst., vol. 51, pp. 224–231, Oct. 2013. doi: 10.1016/j.ijepes.2013.02.030
    [104]
    H. B. Duan, “Aerial robot formation control via pigeon-inspired optimization,” in Robotic Systems: Concepts, Methodologies, Tools, and Applications, Information Resources Management Association, Ed. Hershey, USA: IGI Global, 2020, pp. 1143−1180.
    [105]
    X. L. Zhang, X. F. Chen, and Z. J. He, “An ACO-based algorithm for parameter optimization of support vector machines,” Expert Syst. Appl., vol. 37, no. 9, pp. 6618–6628, Sept. 2010. doi: 10.1016/j.eswa.2010.03.067
    [106]
    S. K. Panda, S. Padhee, A. K. Sood, and S. S. Mahapatra, “Simulation and parameter optimization of flux cored ARC welding using artificial neural network and particle swarm optimization algorithm,” J. Intell. Manuf., vol. 25, no. 1, pp. 67–76, Feb. 2014. doi: 10.1007/s10845-012-0675-0
    [107]
    B. Akay and D. Karaboga, “A modified artificial bee colony algorithm for real-parameter optimization,” Inf. Sci., vol. 192, pp. 120–142, Jun. 2012. doi: 10.1016/j.ins.2010.07.015
    [108]
    Z. Cheng and X. Hong, “PID controller parameters optimization based on artificial fish swarm algorithm,” in Proc. 5th Int. Conf. Intelligent Computation Technology and Automation, Zhangjiajie, China, 2012, pp. 265−268.
    [109]
    S. K. Panda, S. Padhee, and A. K. Sood, S. S. Mahapatra, “Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique,” Intell. Inf. Manage., vol. 1, no. 2, pp. 89–97, Nov. 2009.
    [110]
    L. Ljung, System Identification: Theory for the User. Upper Saddle River, USA: PTR Prentice Hall, 1999, pp. 1−14.
    [111]
    S. H. Tsai and Y. W. Chen, “A novel fuzzy identification method based on ant colony optimization algorithm,” IEEE Access, vol. 4, pp. 3747–3756, Jun. 2016. doi: 10.1109/ACCESS.2016.2585670
    [112]
    A. Alfi and H. Modares, “System identification and control using adaptive particle swarm optimization,” Appl. Math. Model., vol. 35, no. 3, pp. 1210–1221, Mar. 2011. doi: 10.1016/j.apm.2010.08.008
    [113]
    W. Hu, Y. G. Yu, and S. Zhang, “A hybrid artificial bee colony algorithm for parameter identification of uncertain fractional-order chaotic systems,” Nonlinear Dyn., vol. 82, no. 3, pp. 1441–1456, Jul. 2015. doi: 10.1007/s11071-015-2251-6
    [114]
    W. Han, H. H. Wang, and L. Chen, “Parameters identification for photovoltaic module based on an improved artificial fish swarm algorithm,” Sci. World J., vol. 2014, pp. 1–12, Aug. 2014.
    [115]
    B. Majhi and G. Panda, “Development of efficient identification scheme for nonlinear dynamic systems using swarm intelligence techniques,” Expert Syst. Appl., vol. 37, no. 1, pp. 556–566, Jan. 2010. doi: 10.1016/j.eswa.2009.05.036
    [116]
    M. Sonka, V. Havac, and R. Boyle, Image Processing, Analysis, and Machine Vision, Boston, USA: Cengage Learning, 2014.
    [117]
    J. Tian, W. Y. Yu, and S. L. Xie, “An ant colony optimization algorithm for image edge detection,” in Proc. IEEE Congr. Evolutionary Computation, Hong Kong, China, 2008, pp. 751−756.
    [118]
    M. G. H. Omran, A. Salman, and A. P. Engelbrecht, “Dynamic clustering using particle swarm optimization with application in image segmentation,” Pattern Anal. Appl., vol. 8, no. 4, Article No. 332, Feb. 2006. doi: 10.1007/s10044-005-0015-5
    [119]
    A. Draa and A. Bouaziz, “An artificial bee colony algorithm for image contrast enhancement,” Swarm Evol. Comput., vol. 16, pp. 69–84, Jun. 2014. doi: 10.1016/j.swevo.2014.01.003
    [120]
    S. A. El-Said, “Image quantization using improved artificial fish swarm algorithm,” Soft Comput., vol. 19, no. 9, pp. 2667–2679, Sept. 2015. doi: 10.1007/s00500-014-1436-0
    [121]
    M. Hanmandlu, O. P. Verma, N. K. Kumar, and M. Kulkarni, “A novel optimal fuzzy system for color image enhancement using bacterial foraging,” IEEE Trans. Instrum. Meas., vol. 58, no. 8, pp. 2867–2879, Aug. 2009. doi: 10.1109/TIM.2009.2016371
    [122]
    D. Merkle and M. Middendorf, “Swarm intelligence and signal processing [DSP Exploratory],” IEEE Signal Process. Mag., vol. 25, no. 6, pp. 152–158, Nov. 2008. doi: 10.1109/MSP.2008.929839
    [123]
    X. Huang, R. Fernandez-Rojas, K. L. Ou, and A. C. Madoc, “Novel signal processing of brain activity based on ant colony optimization and wavelet analysis with near infrared spectroscopy,” in Proc. 8th IEEE Int. Conf. Communication Software and Networks, Beijing, China, 2016, pp. 81−85.
    [124]
    S. Shadmand and B. Mashoufi, “A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization,” Biomed. Signal Process. Control, vol. 25, pp. 12–23, Mar. 2016. doi: 10.1016/j.bspc.2015.10.008
    [125]
    T. Koza and N. Karaboga, “Quadrature mirror filter bank design for mitral valve doppler signal using artificial bee colony algorithm,” Elektron. Elektrotechn., vol. 23, no. 1, pp. 57–62, Dec. 2017.
    [126]
    M. Y. Jiang and D. F. Yuan, “Wavelet threshold optimization with artificial fish swarm algorithm,” in Proc. Int. Conf. Neural Networks and Brain, Beijing, China, 2005, pp. 569−572.
    [127]
    G. Sahu, B. Biswal, and A. Choubey, “Non‐stationary signal classification via modified fuzzy C‐means algorithm and improved bacterial foraging algorithm,” Int. J. Nume. Model.:Electron. Networks,Devices Fields, vol. 30, no. 2, Mar.–Apr. 2017. doi: 10.1002/jnm.2181

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(2)

    Article Metrics

    Article views (2062) PDF downloads(261) Cited by()

    Highlights

    • Provide a comprehensive survey of swarm intelligence and represent a categorization scheme
    • Describe the achievements of the swarm intelligence in various applications of related fields
    • Summarize the major strengths and limitations, along with main future research trends

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return