Skip to main content
SpringerLink
Log in

Methods for Improving the Efficiency of Swarm Optimization Algorithms. A Survey

  • SURVEY ARTICLES
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

Swarm algorithms belong to the class of population metaheuristic optimization methods. Despite the use of various metaphors, most swarm algorithms have similar structures, where one can distinguish common components such as the decision population initialization, decision diversification, and decision intensification. Based on the concept of generality, an analysis of key approaches to, methods for, and ways of increasing the efficiency of swarm optimization algorithms was carried out. In the survey, swarm optimization algorithms are viewed as a set of operators without a detailed discussion of each algorithm. The main focus is on the analysis of the key components of the algorithms. The main idea behind efficiency improvement is to maintain a balance between diversification and intensification. In this context, we consider mechanisms for supporting population diversity, methods for tuning and adjusting the swarm algorithm parameters, and approaches to hybridization of algorithms. We also indicate several open problems related to the topic of the survey.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

REFERENCES

  1. Li, Q., Liu, S.-Y., and Yang, X.-S., Influence of initialization on the performance of metaheuristic optimizers, Appl. Soft Comput., 2020, vol. 91, article ID 106193.

  2. Luke, S., Essentials of Metaheuristics, lulu.com, 2013. .

  3. Boussaid, I., Lepagnot, J., and Siarry, P., A survey on optimization metaheuristics, Inf. Sci., 2013, vol. 237, pp. 82–117.

    Article  MathSciNet  MATH  Google Scholar 

  4. Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P., Optimization by simulated annealing, Science, 1983, vol. 220, pp. 671–680.

    Article  MathSciNet  MATH  Google Scholar 

  5. Glover, F., Future paths for integer programming and links to artificial intelligence, Comput. Oper. Res., 1986, vol. 13, pp. 533–549.

    Article  MathSciNet  MATH  Google Scholar 

  6. Nematollahi, A.F., Rahiminejad, A., and Vahidi, B., A novel meta-heuristic optimization method based on golden ratio in nature, Soft Comput., 2020, vol. 24, pp. 1117–1151.

    Article  Google Scholar 

  7. Wolpert, D. and Macready, W., No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1997, vol. 1, pp. 67–82.

    Article  Google Scholar 

  8. Clerc, M., From theory to practice in particle swarm optimization, in Handbook of Swarm Intelligence. Vol. 8 , Berlin: Springer, 2011, pp. 3–36.

  9. Sorensen, K., Metaheuristics—the metaphor exposed, Int. Trans. Oper. Res., 2015, vol. 22, no. 1, pp. 3–18.

    Article  MathSciNet  MATH  Google Scholar 

  10. Camacho-Villalon, C.L., Dorigo, M., and Stützle, T., The intelligent water drops algorithm: why it cannot be considered a novel algorithm, Swarm Intell., 2019, vol. 13, pp. 173–192.

    Article  Google Scholar 

  11. Weyland, D., A rigorous analysis of the harmony search algorithm: how the research community can be misled by a “novel” methodology, Int. J. Appl. Metaheuristic Comput., 2010, vol. 1, pp. 50–60.

    Article  Google Scholar 

  12. Geem, Z.W., Kim, J.H., and Loganathan, G.V., A new heuristic optimization algorithm: harmony search, Simulation, 2001, vol. 76, pp. 60–68.

    Article  Google Scholar 

  13. Piotrowski, A.P., Napiorkowski, J.J., and Rowinski, P.M., How novel is the “novel” black hole optimization approach?, Inf. Sci., 2014, vol. 267, pp. 191–200.

    Article  Google Scholar 

  14. Hatamlou, A., Black hole: a new heuristic optimization approach for data clustering, Inf. Sci., 2013, vol. 222, pp. 175–184.

    Article  MathSciNet  Google Scholar 

  15. Shah-Hosseini, H., The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, Int. J. Bio-Inspired Comput., 2009, vol. 1, no. 1/2, pp. 71–79.

    Article  Google Scholar 

  16. Neri, F., Diversity management in memetic algorithms, in Handbook of Memetic Algorithms. Studies in Computational Intelligence. Vol. 379 , Berlin–Heidelberg: Springer, 2012, pp. 153–165.

  17. Cuevas, E., Oliva, D., Zaldivar, D., Perez-Cisneros, M., and Pajares, G., Opposition-based electromagnetism-like for global optimization, Int. J. Innovation Comput. I , 2012, vol. 8, no. 12, pp. 8181–8198.

    Google Scholar 

  18. Turgut, M.S., Turgut, O.E., and Eliiyi, D.T., Island-based crow search algorithm for solving optimal control problems, Appl. Soft Comput., 2020. vol. 90. article ID 106170.

  19. Khodashinskii, I.A. and Dudin, P.A., Identification of fuzzy systems based on direct ant-colony algorithm, Iskusstv. Intellekt Prinyatie Reshenii, 2011, no. 3, pp. 26–33.

  20. Kennedy, J. and Ebenhart, R., Particle swarm optimization, Proc. 1995 IEEE Int. Conf. Neural Networks, Perth: IEEE Serv. Center, 1995, pp. 1942–1948.

  21. Yang, X.-S. and Deb, S., Engineering optimisation by cuckoo search, Int. J. Math. Model. Numer. Optim., 2010, vol. 1, no. 4, pp. 330–343.

    MATH  Google Scholar 

  22. Shirazi, M.Z. et al., Particle swarm optimization with ensemble of inertia weight strategies, in ICSI 2017, Lect. Notes Comput. Sci., Vol. 10385 , Heidelberg: Springer, 2017, pp. 140–147.

  23. Chen, D.B. and Zhao, C.X., Particle swarm optimization with adaptive population size and its application, Appl. Soft Comput., 2009, vol. 9, no. 1, pp. 39–48.

    Article  Google Scholar 

  24. Chen, L., Lu, H., Li, H., Wang, G., and Chen, L., Dimension-by-dimension enhanced cuckoo search algorithm for global optimization, Soft Comput., 2019, vol. 23, pp. 11297–11312.

    Article  Google Scholar 

  25. Singh, A. and Deep, K., Artificial bee colony algorithm with improved search mechanism, Soft Comput., 2019, vol. 23, pp. 12437–12460.

    Article  Google Scholar 

  26. Gupta, S. and Deep, K., Cauchy grey wolf optimiser for continuous optimisation problems, J. Exp. Theor. Artif. Intell., 2018, vol. 30, pp. 1051–1075.

    Article  Google Scholar 

  27. Kazimipour, B., Omidvar, M.N., Li, X., and Qin, A., A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization, Proc. IEEE Congr. Evol. Comput. (Beijing, 2014), pp. 2833–2840.

  28. Pant, M., Thangaraj, R., and Abraha, A., Low discrepancy initialized particle swarm optimization for solving constrained optimization problems, Fundamenta Informaticae, 2009, vol. 95, pp. 511–531.

    Article  MathSciNet  MATH  Google Scholar 

  29. Brits, R., Engelbrecht, A.P., and van den Bergh, F., A niching particle swarm optimizer, Proc. 4th Asia-Pac. Conf. Simul. Evol. Learn. (2002), pp. 692–696.

  30. Altinoz, O.T., Yilmaz, A.E., and Weber, G., Improvement of the gravitational search algorithm by means of low-discrepancy Sobol quasi random-number sequence based initialization, Adv. Electron. Comput. Eng., 2014, vol. 14, no. 3, pp. 55–62.

    Article  Google Scholar 

  31. Mahdavi, S., Rahnamayan, S., and Deb, K., Opposition based learning: a literature review, Swarm Evol. Comput., 2018, vol. 39, pp. 1–23.

    Article  Google Scholar 

  32. Rahnamayan, S., Tizhoosh, H.R., and Salama, M., Opposition-based differential evolution, IEEE Trans. Evol. Comput., 2008, vol. 12, no. 1, pp. 64–79.

    Article  Google Scholar 

  33. Farooq, M.U., Ahmad, A., and Hameed, A., Opposition-based initialization and a modified pattern for inertia weight (IW) in PSO, Proc. IEEE Int. Conf. Innovation Intell. Syst. Appl. (INISTA), 2017, no. 17083603.

  34. Wang, G.-G., Deb, S., Gandomi, A.H., and Alavi, A.H., Opposition-based krill herd algorithm with Cauchy mutation and position clamping, Neurocomputing, 2016, vol. 177, pp. 147–157.

    Article  Google Scholar 

  35. Gao, W.-F., Liu, S.-Y., and Huang, L.-L., Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique, Commun. Nonlinear Sci. Numer. Simul., 2012, vol. 17, pp. 4316–4327.

    Article  MathSciNet  MATH  Google Scholar 

  36. Kazimipour, B., Li, X., and Qin, A.K., A review of population initialization techniques for evolutionary algorithms, Proc. IEEE Congr. Evol. Comput. (Beijing, 2014), pp. 2585–2592.

  37. Melo, V.V. and Botazzo Delbem, A.C., Investigating smart sampling as a population initialization method for differential evolution in continuous problems, Inf. Sci., 2012, vol. 193, pp. 36–53.

    Article  Google Scholar 

  38. Grobler, J. and Engelbrecht, A.P., A scalability analysis of particle swarm optimization roaming behaviour, in ICSI 2017. Lect. Notes Comput. Sci., Vol. 10385 , Heidelberg: Springer, 2017, pp. 119–130.

  39. Gao, W.-F., Liu, S.-Y., and Huang, L.-L., Enhancing artificial bee colony algorithm using more information-based search equations, Inf. Sci., 2014, vol. 270, pp. 112–133.

    Article  MathSciNet  MATH  Google Scholar 

  40. Cheng, S., Shi, Y., and Qin, Q., Experimental study on boundary constraints handling in particle swarm optimization: from population diversity perspective, Int. J. Swarm Intell. Res., 2011, vol. 2, pp. 43–69.

    Article  Google Scholar 

  41. Salleh, M.N.M. et al., Exploration and exploitation measurement in swarm-based metaheuristic algorithms: an empirical analysis, in Recent Advances on Soft Computing and Data Mining, Berlin: Springer, 2018, pp. 24–32.

  42. Chaudhary, R. and Banati, H., Swarm bat algorithm with improved search (SBAIS), Soft Comput., 2019, vol. 23, pp. 11461–11491.

    Article  Google Scholar 

  43. Blackwell, T. and Kennedy, J., Impact of communication topology in particle swarm optimization, IEEE Trans. Evol. Comput., 2019, vol. 23, no. 4, pp. 689–702.

    Article  Google Scholar 

  44. Cheng, S., Shi, Y., and Qin, Q., Promoting diversity in particle swarm optimization to solve multimodal problem, in Neural Inf. Proc., Lect. Notes. Comput. Sci., Vol. 7063 , Heidelberg: Springer, 2011, pp. 228–237.

  45. Kaucic, M., A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization, J. Glob. Optim., 2013, vol. 55, pp. 165–188.

    Article  MathSciNet  MATH  Google Scholar 

  46. Cao, Y. et al., Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions, IEEE Trans. Evol. Comput., 2019, vol. 23, no. 4, pp. 718–731.

    Article  Google Scholar 

  47. Neri, F., Tirronen, V., Karkkainen, T., and Rossi, T., Fitness diversity based adaptation in multimeme algorithms: a comparative study, IEEE Congr. Evol. Comput., Singapore: IEEE, 2007, INSPEC accession no. 9889441.

  48. Ghannami, A., Li, J., Hawbani, A., and Alhusaini, N., Diversity metrics for direct-coded variable-length chromosome shortest path problem evolutionary algorithms, Computing, 2020.

  49. Cheng, S., Shi, Y., Qin, Q., Zhang, Q., and Bai, R., Population diversity maintenance in brain storm optimization algorithm, J. Artif. Intell. Soft Comput. Res., 2014, vol. 4, no. 2, pp. 83–97.

    Article  Google Scholar 

  50. Simon, D., Omran, M.G., and Clerc, M., Linearized biogeography-based optimization with re-initialization and local search, Inf. Sci., 2014, vol. 267, pp. 140–157.

    Article  MathSciNet  Google Scholar 

  51. Kennedy, D.D., Zhang, H., Rangaiah, G.P., and Bonilla-Petriciolet, A., Particle swarm optimization with re-initialization strategies for continuous global optimization, in Global Optimization: Theory, Developments and Applications, Nova Science Publ., 2013, pp. 1–42.

  52. Merrikh-Bayat, F., The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature, Appl. Soft Comput., 2015, vol. 33, pp. 292–303.

    Article  Google Scholar 

  53. Saxena, A., A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm, Expert Syst. Appl., 2019, vol. 132, pp. 166–188.

    Article  Google Scholar 

  54. Wang, G.-G. and et., al., Chaotic krill herd algorithm, Inf. Sci., 2014, vol. 274, pp. 17–34.

    Article  MathSciNet  Google Scholar 

  55. Wang, G.-G., Deb, S., Zhao, X., and Cui, Z., A new monarch butterfly optimization with an improved crossover operator, Oper. Res. Int. J., 2018, vol. 18, pp. 731–755.

    Article  Google Scholar 

  56. Zhou, L., Ma, M., Ding, L., and Tang, W., Centroid opposition with a two-point full crossover for the partially attracted firefly algorithm, Soft Comput., 2019, vol. 23, pp. 12241–12254.

    Article  Google Scholar 

  57. San, P.P., Ling, S.H., and Nguyen, H.T., Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system, Neural Comput. Appl., 2013, vol. 23, pp. 2177–2184.

    Article  Google Scholar 

  58. Saha, S.K., Kar, R., Mandal, D., and Ghoshal, S.P., Optimal IIR filter design using gravitational search algorithm with wavelet mutation, J. King Saud Univ. Comput. Inf. Sci., 2015, vol. 27, pp. 25–39.

    Google Scholar 

  59. Nobahari, H., Nikusokhan, M., and Siarry, P., A multi-objective gravitational search algorithm based on non-dominated sorting, Int. J. Swarm. Intell. Res., 2012, vol. 3, pp. 32–49.

    Article  Google Scholar 

  60. Chen, Y. et al., Dynamic multi-swarm differential learning particle swarm optimizer, Swarm Evol. Comput., 2018, vol. 39, pp. 209–221.

    Article  Google Scholar 

  61. Yazdani, S. and Hadavandi, E., LMBO-DE: a linearized monarch butterfly optimization algorithm improved with differential evolution, Soft Comput., 2019, vol. 23, pp. 8029–8043.

    Article  Google Scholar 

  62. Luo, J. and Liu, Z., Novel grey wolf optimization based on modified differential evolution for numerical function optimization, Appl. Intell., 2020, vol. 50, pp. 468–486.

    Article  Google Scholar 

  63. Zou, F. et al., Teaching-learning-based optimization with differential and repulsion learning for global optimization and nonlinear modeling, Soft Comput., 2018, vol. 22, pp. 7177–7205.

    Article  Google Scholar 

  64. Mekh, M.A. and Hodashinsky, I.A., Comparative analysis of differential evolution methods to optimize parameters of fuzzy classifiers, J. Comput. Syst. Sci. Int., 2017, vol. 56, no. 4, pp. 616–626.

    Article  MATH  Google Scholar 

  65. Herrera, F., Lozano, M., and Sanchez, A.M., A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study, Int. J. Intell. Syst., 2003, vol. 18, pp. 309–338.

    Article  MATH  Google Scholar 

  66. Gao, S. et al., Gravitational search algorithm combined with chaos for unconstrained numerical optimization, Appl. Math. Comput., 2014, vol. 231, pp. 48–62.

    MathSciNet  MATH  Google Scholar 

  67. Metlicka, M. and Davendra, D., Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems, Swarm Evol. Comput., 2015, vol. 25, pp. 15–28.

    Article  Google Scholar 

  68. Pluhacek, M., Senkerik, R., and Davendra, D., Chaos particle swarm optimization with ensemble of chaotic systems, Swarm Evol. Comput., 2015, vol. 25, pp. 29–35.

    Article  Google Scholar 

  69. Ma, H. et al., Multi-population techniques in nature inspired optimization algorithms: a comprehensive survey, Swarm Evol. Comput., 2019, vol. 44, pp. 365–387.

    Article  Google Scholar 

  70. Blackwell, T. and Branke, J., Multiswarms, exclusion, and anti-convergence in dynamic environments, IEEE Trans. Evol. Comput., 2006, vol. 10, pp. 459–472.

    Article  Google Scholar 

  71. Lung, R.I. and Dumitrescu, D., Evolutionary swarm cooperative optimization in dynamic environments, Nat. Comput., 2010, vol. 9, pp. 83–94.

    Article  MathSciNet  MATH  Google Scholar 

  72. Corcoran, A.L. and Wainwright, R.L., A parallel island model genetic algorithm for the multiprocessor scheduling problem, Proc. ACM Symp. Appl. Comput. ACM. (1994), pp. 483–487.

  73. Lardeux, F., Maturana, J., Rodriguez-Tello, E., and Saubion, F., Migration policies in dynamic island models, Nat. Comput., 2019, vol. 18, pp. 163–179.

    Article  MathSciNet  Google Scholar 

  74. Sutton, R. and Barto, A., Reinforcement Learning: an Introduction, London: MIT Press, 1998.

    MATH  Google Scholar 

  75. Gong, Y.J. et al., Distributed evolutionary algorithms and their models: a survey of the state-of-the-art, Appl. Soft Comput., 2015, vol. 34, pp. 286–300.

    Article  Google Scholar 

  76. Raidl, G.R., A unified view on hybrid metaheuristics, in Proc. Hybrid Metaheuristics, Third Int. Workshop, Lect. Notes. Comput. Sci., Vol. 4030., Heidelberg: Springer, 2006, pp. 1–12.

  77. Hodashinsky, I.A. and Gorbunov, I.V., Hybrid method of building fuzzy systems based on island model, Inf. Sist. Upr., 2014, no. 3, pp. 114–120.

  78. Lynn, N., Ali, M.Z, and Suganthan, P.N., Population topologies for particle swarm optimization and differential evolution, Swarm Evol. Comput., 2018, vol. 39, pp. 24–35.

    Article  Google Scholar 

  79. Shi, Y., Liu, H., Gao, L., and Zhang, G., Cellular particle swarm optimization, Inf. Sci., 2011, vol. 181, pp. 4460–4493.

    Article  MathSciNet  MATH  Google Scholar 

  80. Fang, W., Sun, J., Chen, H., and Wu, X., A decentralized quantum-inspired particle swarm optimization algorithm with cellular structured population, Inf. Sci., 2016, vol. 330, pp. 19–48.

    Article  Google Scholar 

  81. Huang, L. and Qin, C., A novel modified gravitational search algorithm for the real world optimization problem, Int. J. Mach. Learn. Cybern., 2019, vol. 10, pp. 2993–3002.

    Article  Google Scholar 

  82. Li, C. and Yang, S., A general framework of multipopulation methods with clustering in undetectable dynamic environments, IEEE Trans. Evol. Comput., 2012, vol. 16, pp. 556–577.

    Article  Google Scholar 

  83. Xia, L., Chu, J., and Geng, Z., A multiswarm competitive particle swarm algorithm for optimization control of an ethylene cracking furnace, Appl. Artif. Intell., 2014, vol. 28, pp. 30–46.

    Article  Google Scholar 

  84. Huang, C., Li, Y., and Yao, X., A survey of automatic parameter tuning methods for metaheuristics, IEEE Trans. Evol. Comput., 2020, vol. 24, no. 2, pp. 201–216.

    Article  Google Scholar 

  85. Poli, R., Mean and variance of the sampling distribution of particle swarm optimizers during stagnation, IEEE Trans. Evol. Comput., 2009, vol. 13, no. 4, pp. 712–721.

    Article  Google Scholar 

  86. Sengupta, S., Basak, S., and Peters, R.A., Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives, Mach. Learn. Knowl. Extr., 2019, vol. 1, pp. 157–191.

    Article  Google Scholar 

  87. Calvet, L., Juan, A.A., Serrat, C., and Ries, J., A statistical learning based approach for parameter fine-tuning of metaheuristics, SORT—Stat. Oper. Res. Trans., 2016, vol. 40, pp. 201–224.

    MathSciNet  MATH  Google Scholar 

  88. Birattari, M., Tuning metaheuristics: a machine learning perspective, SCI., 2009, vol. 197.

  89. Hutter, F., Hoos, H.H., Leyton-Brown, K., and Stützle, T., ParamILS: an automatic algorithm configuration framework, J. Artif. Intell. Res., 2009, vol. 36, pp. 267–306.

    Article  MATH  Google Scholar 

  90. Yuan, Z., de Oca, M.M.A., Birattari, M., and Stützle, T., Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms, Swarm Intell., 2012, vol. 6, pp. 49–75.

    Article  Google Scholar 

  91. Eiben, A.E. and Smit, S.K., Evolutionary algorithm parameters and methods to tune them, in Autonomous Search, Berlin: Springer, 2012, pp. 15–36.

  92. Adenso-Diaz, B. and Laguna, M., Fine-tuning of algorithms using fractional experimental designs and local search, Oper. Res., 2006, vol. 54, pp. 99–114.

    Article  MATH  Google Scholar 

  93. Barbosa, E.B.M. and Senne, E.L.F., Improving the fine-tuning of metaheuristics: an approach combining design of experiments and racing algorithms, J. Optim., 2017, vol. 2017, pp. 1–7.

    MathSciNet  MATH  Google Scholar 

  94. Fallahi, M., Amiri, S., and Yaghini, M., A parameter tuning methodology for metaheuristics based on design of experiments, Int. J. Eng. Tech. Sci., 2014, vol. 2, pp. 497–521.

    Google Scholar 

  95. Huang, D., Allen, T.T., Notz, W.I., and Zeng, N., Global optimization of stochastic black-box systems via sequential kriging meta-models, J. Glob. Optim., 2006, vol. 34, no. 3, pp. 441–466.

    Article  MathSciNet  MATH  Google Scholar 

  96. Audet, C. and Dennis, J.E., Mesh adaptive direct search algorithms for constrained optimization, SIAM J. Optim., 2006, vol. 17, no. 1, pp. 188–217.

    Article  MathSciNet  MATH  Google Scholar 

  97. Montero, E., Riff, M.-C., and Neveu, B., A beginner’s guide to tuning methods, Appl. Soft. Comput., 2014, vol. 17, pp. 39–51.

    Article  Google Scholar 

  98. Karafotias, G., Hoogendoorn, M., and Eiben, A.E., Parameter control in evolutionary algorithms: trends and challenges, IEEE Trans. Evol. Comput., 2015, vol. 19, no. 2, pp. 167–187.

    Article  Google Scholar 

  99. Zhang, J., Chen, W.-N., Zhan, Z.-H., Yu, W.-J., Li, Y.-L., Chen, N., and Zhou, Q., A survey on algorithm adaptation in evolutionary computation, Front. Electr. Electron. Eng., 2012, vol. 7, no. 1, pp. 16–31.

    Article  Google Scholar 

  100. Harrison, K.R., Engelbrecht, A.P., and Ombuki-Berman, B.M., Inertia weight control strategies for particle swarm optimization, Swarm. Intell., 2016, vol. 10, no. 4, pp. 267–305.

    Article  Google Scholar 

  101. Eberhart, R.C. and Shi, Y., Tracking and optimizing dynamic systems with particle swarms, Proc. IEEE Congr. Evol. Comput. (Seoul, South Korea, 2001), vol. 1, pp. 94–100.

  102. Shi, Y. and Eberhart, R.C., Empirical study of particle swarm optimization, Proc. IEEE Congr. Evol. Comput. (1999), vol. 3, pp. 1945–1950.

  103. Yang, C., Gao, W., Liu, N., and Song, C., Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight, Appl. Soft Comput., 2015, vol. 29, pp. 386–394.

    Article  Google Scholar 

  104. Jiao, B., Lian, Z., and Gu, X., A dynamic inertia weight particle swarm optimization algorithm, Chaos Solitons Fractals, 2008, vol. 37, no. 3, pp. 698–705.

    Article  MATH  Google Scholar 

  105. Fan, S.K.S. and Chiu, Y.Y., A decreasing inertia weight particle swarm optimizer, Eng. Optim., 2007, vol. 39, no. 2, pp. 203–228.

    Article  MathSciNet  Google Scholar 

  106. Chauhan, P., Deep, K., and Pant, M., Novel inertia weight strategies for particle swarm optimization, Memetic Comput., 2013, vol. 5, no. 3, pp. 229–251.

    Article  Google Scholar 

  107. Chen, G., Huang, X., Jia, J., and Min, Z., Natural exponential inertia weight strategy in particle swarm optimization, Proc. Sixth World Congr. Intell. Control Autom. (2006), IEEE, vol. 1, pp. 3672–3675.

  108. Gao, Y.-L., An, X.-H., and Liu, J.-M., A particle swarm optimization algorithm with logarithm decreasing inertia weight and chaos mutation, Int. Conf. Comput. Intell. Security (2008), IEEE, vol. 1, pp. 61–65.

  109. Feng, Y., Teng, G.F., Wang, A.X., and Yao, Y.M., Chaotic inertia weight in particle swarm optimization, Proc. Second Int. Conf. Innovative Comput. Inf. Control, Kumamoto: IEEE, 2007, pp. 475–479.

  110. Kentzoglanakis, K. and Poole, M., Particle swarm optimization with an oscillating inertia weight, Proc. 11th Annu. Conf. Genetic Evol. Comput. ACM (2009), pp. 1749–1750.

  111. Ratnaweera, A., Halgamuge, S.K., and Watson, H.C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans. Evol. Comput., 2004, vol. 8, pp. 240–255.

    Article  Google Scholar 

  112. Tanweer, M.R., Suresh, S., and Sundararajan, N., Self regulating particle swarm optimization algorithm, Inf. Sci., 2015, vol. 294, pp. 182–202.

    Article  MathSciNet  MATH  Google Scholar 

  113. Xu, G., An adaptive parameter tuning of particle swarm optimization algorithm, Appl. Math. Comput., 2013, vol. 219, pp. 4560–4569.

    MathSciNet  MATH  Google Scholar 

  114. Adewumi, A.O. and Arasomwan, A.M., An improved particle swarm optimiser based on swarm success rate for global optimisation problems, J. Exp. Theor. Artif. Intell., 2014, vol. 28, pp. 441–483.

    Article  Google Scholar 

  115. Yang, X., Yuan, J., and Mao, H., A modified particle swarm optimizer with dynamic adaptation, Appl. Math. Comput., 2007, vol. 189, pp. 1205–1213.

    MathSciNet  MATH  Google Scholar 

  116. Zhan, Z.-H., Zhang, J., Li, Y., and Chung, H.S.-H., Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern. B, 2009, vol. 39, pp. 1362–1381.

    Article  Google Scholar 

  117. Huang, L., Ding, S., Yu, S., Wang, J., and Lu, K., Chaos-enhanced cuckoo search optimization algorithms for global optimization, Appl. Math. Model., 2016, vol. 40, pp. 3860–3875.

    Article  MathSciNet  MATH  Google Scholar 

  118. Mirjalili, S. and Gandomi, A.H., Chaotic gravitational constants for the gravitational search algorithm, Appl. Soft Comput., 2017, vol. 53, pp. 407–419.

    Article  Google Scholar 

  119. Gandomi, A., Yang, X.-S., Talatahari, S., and Alavi, A., Firefly algorithm with chaos, Commun. Nonlinear Sci.Numer. Simul., 2013, vol. 18, pp. 89–98.

    Article  MathSciNet  MATH  Google Scholar 

  120. Gandomi, A.H. and Yang, X.-S., Chaotic bat algorithm, J. Comput. Sci., 2014, vol. 5, pp. 224–232.

    Article  MathSciNet  Google Scholar 

  121. Juang, Y.-T., Tung, S.-L., and Chiu, H.-C., Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions, Inf. Sci., 2011, vol. 181, pp. 4539–4549.

    Article  MathSciNet  MATH  Google Scholar 

  122. Melin, P. et al., Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic, Expert Syst. Appl., 2013, vol. 40, pp. 3196–3206.

    Article  Google Scholar 

  123. Neshat, M., FAIPSO: fuzzy adaptive informed particle swarm optimization, Neural Comput. Appl., 2013, vol. 23, pp. 95–116.

    Article  Google Scholar 

  124. Gonzalez, B., Melin, P., Valdez, F., and Prado-Arechiga, G., A gravitational search algorithm using fuzzy adaptation of parameters for optimization of ensemble neural networks in medical imaging, Proc. Inter. Conf. Artif. Intell. (2017), Las Vegas: CSREA Press, 2017, pp. 54–59.

  125. Perez, J. et al., Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm, Soft Comput., 2017, vol. 21, pp. 667–685.

    Article  Google Scholar 

  126. Amador-Angulo, L. and Castillo, O., Statistical analysis of type-1 and interval type-2 fuzzy logic in dynamic parameter adaptation of the BCO, Proc. 9th Conf. Eur. Soc. Fuzzy Logic Technol. (2015), Atlantis Press, 2015, pp. 776–783.

  127. Abdel-Basset, M., Wang, G.-G., Sangaiah, A.K., and Rushdy, E., Krill herd algorithm based on cuckoo search for solving engineering optimization problems, Multimedia Tools Appl., 2019, vol. 78, pp. 3861–3884.

    Article  Google Scholar 

  128. Galvez, J., Cuevas, E., Becerra, H., and Avalos, O., A hybrid optimization approach based on clustering and chaotic sequences, Int. J. Mach. Learn. Cybern., 2020, vol. 11, pp. 359–401.

    Article  Google Scholar 

  129. Caraffini, F., Neri, F., and Epitropakis, M., HyperSPAM: a study on hyper-heuristic coordination strategies in the continuous domain, Inf. Sci., 2019, vol. 477, pp. 186–202.

    Article  Google Scholar 

  130. Chen, X., Ong, Y.-S., Lim, M.-H., and Tan, K.C., A multi-facet survey on memetic computation, IEEE Trans. Evol. Comput., 2011, vol. 15, pp. 591–607.

    Article  Google Scholar 

  131. Bartoccini, U., Carpi, A., Poggioni, V., and Santucci, V., Memes evolution in a memetic variant of particle swarm optimization, Mathematics, 2019, vol. 7, p. 423.

    Article  Google Scholar 

  132. Duan, Q., Liao, T.W., and Yi, H.Z., A comparative study of different local search application strategies in hybrid metaheuristics, Appl. Soft Comput., 2013, vol. 13, pp. 1464–1477.

    Article  Google Scholar 

  133. Lopez-Garcia, M., Garcia-Rodenas, R., and Gonzalez, A.G., Hybrid meta-heuristic optimization algorithms for time-domain-constrained data clustering, Appl. Soft Comput., 2014, vol. 23, pp. 319–332.

    Article  Google Scholar 

  134. de Oca, M.A.M, Cotta, C., and Neri, F., Local search, in Handbook of Memetic Algorithms. Studies in Computational Intelligence. Vol. 379 , Berlin–Heidelberg: Springer, 2012, pp. 29–41.

  135. Lai, X. and Hao, J.-K., A tabu search based memetic algorithm for the max-mean dispersion problem, Comput. Oper. Res., 2016, vol. 72, pp. 118–127.

    Article  MATH  Google Scholar 

  136. Yu, Y. et al., CBSO: a memetic brain storm optimization with chaotic local search, Memetic Comput., 2018, vol. 10, pp. 353–367.

    Article  Google Scholar 

  137. Petalas, Y.G., Parsopoulos, K.E., and Vrahatis, M.N., Memetic particle swarm optimization, Ann. Oper. Res., 2007, vol. 156, pp. 99–127.

    Article  MathSciNet  MATH  Google Scholar 

  138. Wang, H., Moon, I., Yang, S., and Wang, D., A memetic particle swarm optimization algorithm for multimodal optimization problems, Inf. Sci., 2012, vol. 197, pp. 38–52.

    Article  Google Scholar 

  139. Fister, I., Fister, I., Jr., Brest, J., and Zumer, V., Memetic artificial bee colony algorithm for large-scale global optimization, 2012 IEEE Congr. Evol. Comput., Brisbane: IEEE, 2012, pp. 1–8.

  140. Sudholt, D., Parametrization and balancing local and global search, in Handbook of Memetic Algorithms. Studies in Computational Intelligence. Vol. 379 , Berlin–Heidelberg: Springer, 2012, pp. 55–72.

  141. Wu, G., Mallipeddi, R., and Suganthan, P.N., Ensemble strategies for population-based optimization algorithms—a survey, Swarm Evol. Comput., 2019, vol. 44, pp. 695–711.

    Article  Google Scholar 

  142. Li, C., Yang, S., and Nguyen, T.T., A Self-learning particle swarm optimizer for global optimization problems, IEEE Trans. Syst. Man Cybern. Part B., 2012, vol. 42, pp. 627–646.

    Article  Google Scholar 

  143. Wang, H. et al., Multi-strategy ensemble artificial bee colony algorithm, Inf. Sci., 2014, vol. 279, pp. 587–603.

    Article  MathSciNet  MATH  Google Scholar 

  144. Lynn, N. and Suganthan, P.N., Ensemble Particle Swarm Optimizer, Appl. Soft Comput., 2017, vol. 55, pp. 533–548.

    Article  Google Scholar 

  145. Pillay, N. and Qu, R., Hyper-Heuristics: Theory and Applications, Cham: Springer, 2018.

    Book  Google Scholar 

  146. Del Ser, J. et al., Bio-inspired computation: where we stand and what’s next, Swarm Evol. Comput., 2019, vol. 48, pp. 220–250.

    Article  Google Scholar 

  147. Elaziz, M.A. and Mirjalili, S., A hyper-heuristic for improving the initial population of whale optimization algorithm, Knowl. Based Syst., 2019, vol. 172, pp. 42–63.

    Article  Google Scholar 

  148. Miranda, P., Prudencio, R., and Pappa, G., H3ad: a hybrid hyper-heuristic for algorithm design, Inf. Sci., 2017, vol. 414, pp. 340–354.

    Article  Google Scholar 

  149. Yang, X.-S., Nature-inspired optimization algorithms: challenges and open problems, J. Comput. Sci., 2020, article ID 101104.

  150. Bassimir, B., Schmitt, M., and Wanka, R., Self-adaptive potential-based stopping criteria for particle swarm optimization with forced moves, Swarm. Intell., 2020, vol. 14, pp. 285–311.

    Article  Google Scholar 

  151. Li, P. and Zhao, Y., A quantum-inspired vortex search algorithm with application to function optimization, Nat. Comput., 2019, vol. 18, pp. 647–674.

    Article  MathSciNet  Google Scholar 

  152. Nedjah, N. and Mourelle, L.M., Hardware for Soft Computing and Soft Computing for Hardware. Studies in Computational Intelligence. Vol. 529 , Cham: Springer, 2014.

    Book  Google Scholar 

  153. Li, D. et al., A general framework for accelerating swarm intelligence algorithms on FPGAs, GPUs and multi-core CPUs, IEEE Access., 2018, vol. 6, pp. 72327–72344.

    Article  Google Scholar 

  154. Damaj, I., Elshafei, M., El-Abd, M., and EminAydin, M., An analytical framework for high-speed hardware particle swarm optimization, Microprocess Microsyst. 2020, vol. 72, article ID 102949.

Download references

Funding

This work was supported by the Russian Foundation for Basic Research, project no.19-17-50050.

Author information

Authors and Affiliations

  1. Tomsk State University of Control Systems and Radioelectronics, Tomsk, 634050, Russia

    I. A. Hodashinsky

Authors
  1. I. A. Hodashinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. A. Hodashinsky.

Additional information

Translated by V. Potapchouck

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hodashinsky, I.A. Methods for Improving the Efficiency of Swarm Optimization Algorithms. A Survey. Autom Remote Control 82, 935–967 (2021). https://doi.org/10.1134/S0005117921060011

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117921060011

Keywords

Search

Navigation