Neural Computing and Applications

, Volume 32, Issue 2, pp 567–588 | Cite as

A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms

  • Han Duy PhanEmail author
  • Kirsten Ellis
  • Jan Carlo Barca
  • Alan Dorin
Review Article


Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.


Swarm intelligence algorithms Dynamic parameter setting Parameter control 


Compliance with ethical standards

Conflict of interest

The authors Han Phan, Kirsten Ellis, Jan Carlo Barca declare that they have no conflict of interest. The author Alan Dorin is a member of the editorial board for the journal Neural Computing and Applications.


  1. 1.
    Abdullah S, Alzaqebah M (2013) A hybrid self-adaptive bees algorithm for examination timetabling problems. Appl Soft Comput 13(8):3608–3620CrossRefGoogle Scholar
  2. 2.
    Angeline PJ (1998) Using selection to improve particle swarm optimization. In: Proceedings of IEEE international conference on evolutionary computation, Citeseer, pp 84–89Google Scholar
  3. 3.
    Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47CrossRefGoogle Scholar
  4. 4.
    Bartz-Beielstein T, Parsopoulos KE, Vrahatis MN (2004) Analysis of particle swarm optimization using computational statistics. In: Proceedings of the international conference of numerical analysis and applied mathematics (ICNAAM 2004), pp 34–37Google Scholar
  5. 5.
    Beielstein T, Parsopoulos KE, Vrahatis MN (2002) Tuning pso parameters through sensitivity analysis. Universität Dortmund, Tech. repGoogle Scholar
  6. 6.
    Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: Proceedings of the 4th annual conference on genetic and evolutionary computation, Morgan Kaufmann Publishers Inc., pp 11–18Google Scholar
  7. 7.
    Biswas A, Dasgupta S, Das S, Abraham A (2007) Synergy of PSO and bacterial foraging optimization a comparative study on numerical benchmarks. Innovations in hybrid intelligent systems. Springer, Berlin, pp 255–263CrossRefGoogle Scholar
  8. 8.
    Blackwell T (2007) Particle swarm optimization in dynamic environments. Evolutionary computation in dynamic and uncertain environments. Springer, Berlin, pp 29–49CrossRefGoogle Scholar
  9. 9.
    Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. In: Raidl GR et al (eds) Applications of evolutionary computing, vol 3005. EvoW6orkshops. Springer, Berlin, pp 489–500CrossRefGoogle Scholar
  10. 10.
    Blackwell T, Branke J, Li X (2008) Particle swarms for dynamic optimization problems. Swarm intelligence. Springer, Berlin, pp 193–217CrossRefGoogle Scholar
  11. 11.
    Blackwell TM, Bentley PJ et al (2002) Dynamic search with charged swarms. In: GECCO, Citeseer, vol 2, pp 19–26Google Scholar
  12. 12.
    Box GEP, Hunter JS, Hunter WG (2005) Statistics for experimenters: design, innovation, and discovery, 2nd edn. Wiley, New YorkzbMATHGoogle Scholar
  13. 13.
    Cáceres LP, López-Ibáñez M, Stützle T (2015) Ant colony optimization on a limited budget of evaluations. Swarm Intell 9(2–3):103–124CrossRefGoogle Scholar
  14. 14.
    Castellani M, Pham QT, Pham DT (2012) Dynamic optimisation by a modified bees algorithm. Proc Inst Mech Eng Part I J Syst Control Eng 226(7):956–971CrossRefGoogle Scholar
  15. 15.
    Chen XH, Lee WP, Liao CY, Dai JT (2007) Adaptive constriction factor for location-related particle swarm. In: Proceedings of the 8th Conference on 8th WSEAS international conference on evolutionary computing, vol 8. World Scientific and Engineering Academy and Society (WSEAS), pp 307–313Google Scholar
  16. 16.
    Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 3. IEEE, pp 1951–1957Google Scholar
  17. 17.
    Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  18. 18.
    Collins LM, Dziak JJ, Li R (2009) Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psychol Methods 14(3):202CrossRefGoogle Scholar
  19. 19.
    Darwin C (1859) On the origin of species by means of natural selection. Murray, LondonGoogle Scholar
  20. 20.
    Das S, Mullick SS, Suganthan P (2016) Recent advances in differential evolution—an updated survey. Swarm Evol Comput 27:1–30CrossRefGoogle Scholar
  21. 21.
    Dasgupta S, Das S, Abraham A, Biswas A (2009) Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans Evol Comput 13(4):919–941CrossRefGoogle Scholar
  22. 22.
    Dasgupta S, Das S, Biswas A, Abraham A (2010) Automatic circle detection on digital images with an adaptive bacterial foraging algorithm. Soft Comput 14(11):1151–1164CrossRefGoogle Scholar
  23. 23.
    Deb K (1995) Optimization for engineering design. Prentice-Hall, IndiaGoogle Scholar
  24. 24.
    Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195CrossRefGoogle Scholar
  25. 25.
    Dorigo M (1992) Optimization, learning and natural algorithms. PhD thesis, Politecnico di Milano, ItalyGoogle Scholar
  26. 26.
    Dorigo M, Gambardella LM (1997) Ant colonies for the travelling salesman problem. BioSystems 43(2):73–81CrossRefGoogle Scholar
  27. 27.
    Dorigo M, Stützle T (2009) Ant colony optimization: overview and recent advances. Techreport, IRIDIA, Universite Libre de BruxellesGoogle Scholar
  28. 28.
    Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41CrossRefGoogle Scholar
  29. 29.
    Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation, vol 1. IEEE, pp 84–88Google Scholar
  30. 30.
    Eiben AE, Smit SK (2011a) Evolutionary algorithm parameters and methods to tune them. Autonomous search. Springer, Berlin, pp 15–36CrossRefGoogle Scholar
  31. 31.
    Eiben AE, Smit SK (2011b) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1(1):19–31CrossRefGoogle Scholar
  32. 32.
    Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141CrossRefGoogle Scholar
  33. 33.
    El-Gallad A, El-Hawary M, Sallam A, Kalas A (2002) Enhancing the particle swarm optimizer via proper parameters selection. In: Canadian conference on electrical and computer engineering, IEEE CCECE 2002, vol 2. IEEE, Canada, pp 792–797Google Scholar
  34. 34.
    Erskine A, Herrmann JM (2014) Crips: Critical dynamics in particle swarm optimization. arXiv preprint arXiv:14026888
  35. 35.
    Esmin AA, Coelho RA, Matwin S (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif Intell Rev 44(1):23–45CrossRefGoogle Scholar
  36. 36.
    Fan H, Shi Y (2001) Study on vmax of particle swarm optimization. In: Proc. Workshop on particle swarm optimization, Purdue School of Engineering and TechnologyGoogle Scholar
  37. 37.
    Farhat I, El-Hawary M (2010) Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power. IET Gener Transm Distrib 4(9):989–999CrossRefGoogle Scholar
  38. 38.
    Favaretto D, Moretti E, Pellegrini P (2009) On the explorative behavior of max–min ant system. In: International workshop on engineering stochastic local search algorithms. Springer, pp 115–119Google Scholar
  39. 39.
    Fister Jr I, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:13074186
  40. 40.
    Flood MM (1956) The traveling-salesman problem. Oper Res 4(1):61–75MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W. H. Freeman & Co., New YorkzbMATHGoogle Scholar
  42. 42.
    Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72CrossRefGoogle Scholar
  43. 43.
    Hu M, Wu T, Weir JD (2012) An intelligent augmentation of particle swarm optimization with multiple adaptive methods. Inf Sci 213:68–83CrossRefGoogle Scholar
  44. 44.
    Hu M, Wu TF, Weir JD (2013) An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evol Comput 17(5):705–720CrossRefGoogle Scholar
  45. 45.
    Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Evolutionary computation. IEEE, pp 1677–1681Google Scholar
  46. 46.
    Hussain K, Salleh MNM, Cheng S, Shi Y (2018) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl. CrossRefGoogle Scholar
  47. 47.
    Hussein WA, Sahran S, Abdullah SNHS (2014) Patch-levy-based initialization algorithm for bees algorithm. Appl Soft Comput 23:104–121CrossRefGoogle Scholar
  48. 48.
    Hussein WA, Sahran S, Sheikh Abdullah S (2015) An improved bees algorithm for real parameter optimization. Int J Adv Comput Sci Appl 6:23–39Google Scholar
  49. 49.
    Jevtié A, Andina D (2010) Adaptive artificial ant colonies for edge detection in digital images. In: IECON 2010-36th annual conference on IEEE industrial electronics society. IEEE, pp 2813–2816Google Scholar
  50. 50.
    Jhang JY, Lin CJ, Lin CT, Young KY (2018) Navigation control of mobile robots using an interval type-2 fuzzy controller based on dynamic-group particle swarm optimization. Int J Control Autom Syst 16(5):2446–2457CrossRefGoogle Scholar
  51. 51.
    Jiao R, Sun Y, Sun J, Jiang Y, Zeng S (2018) Antenna design using dynamic multi-objective evolutionary algorithm. IET Microw Antennas Propag 12(13):2065–2072CrossRefGoogle Scholar
  52. 52.
    Karafotias G, Hoogendoorn M, Eiben ÁE (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187CrossRefGoogle Scholar
  53. 53.
    Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: IEEE international conference on evolutionary computation, 1997. IEEE, pp 303–308Google Scholar
  54. 54.
    Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 congress on evolutionary computation, 1999, CEC 99, vol 3. IEEE, pp 1931–1938Google Scholar
  55. 55.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings, IEEE international conference on neural networks, vol 4. IEEE, pp 1942–1948Google Scholar
  56. 56.
    Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 congress on evolutionary computation, CEC’02, vol 2. IEEE, pp 1671–1676Google Scholar
  57. 57.
    Kennedy J, Kennedy JF, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann, BurlingtonGoogle Scholar
  58. 58.
    Khanmirzaei Z, Teshnehlab M, Sharifi A (2010) Modified honey bee optimization for recurrent neuro-fuzzy system model. In: 2010 The 2nd international conference on computer and automation engineering (ICCAE), vol 5. IEEE, pp 780–785Google Scholar
  59. 59.
    Kiranyaz S, Pulkkinen J, Gabbouj M (2011) Multi-dimensional particle swarm optimization in dynamic environments. Expert Syst Appl 38(3):2212–2223CrossRefGoogle Scholar
  60. 60.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, CambridgezbMATHGoogle Scholar
  61. 61.
    Kramer O (2010) Evolutionary self-adaptation: a survey of operators and strategy parameters. Evol Intell 3(2):51–65zbMATHCrossRefGoogle Scholar
  62. 62.
    Krohling RA (2005) Gaussian particle swarm with jumps. In: The 2005 IEEE congress on evolutionary computation, vol 2. IEEE, pp 1226–1231Google Scholar
  63. 63.
    Langton CG (1990) Computation at the edge of chaos: phase transitions and emergent computation. Phys D Nonlinear Phenom 42(1):12–37MathSciNetCrossRefGoogle Scholar
  64. 64.
    Li G, Qian C, Jiang C, Lu X, Tang K (2018) Optimization based layer-wise magnitude-based pruning for dnn compression. In: IJCAI, pp 2383–2389Google Scholar
  65. 65.
    Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRefGoogle Scholar
  66. 66.
    Lin FT, Kao CY, Hsu CC (1993) Applying the genetic approach to simulated annealing in solving some np-hard problems. IEEE Trans Syst Man Cybern 23(6):1752–1767CrossRefGoogle Scholar
  67. 67.
    Lin JH, Chou CW, Yang CH, Tsai HL et al (2012) A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. J Comput Inf Technol 2(2):56–63Google Scholar
  68. 68.
    López-Ibánez M, Dubois-Lacoste J, Stützle T, Birattari M (2011) The irace package, iterated race for automatic algorithm configuration. Tech. rep, CiteseerGoogle Scholar
  69. 69.
    López-Ibáñez M, Dubois-Lacoste J, Cáceres LP, Birattari M, Stützle T (2016) The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect 3:43–58MathSciNetCrossRefGoogle Scholar
  70. 70.
    Lovbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimiser with breeding and subpopulations. Proc Genetic Evol Comput Conf Citeseer 2001:469–476Google Scholar
  71. 71.
    Majhi R, Panda G, Majhi B, Sahoo G (2009) Efficient prediction of stock market indices using adaptive bacterial foraging optimization (abfo) and bfo based techniques. Expert Syst Appl 36(6):10097–10104CrossRefGoogle Scholar
  72. 72.
    Melin P, Olivas F, Castillo O, Valdez F, Soria J, Valdez M (2013) Optimal design of fuzzy classification systems using pso with dynamic parameter adaptation through fuzzy logic. Expert Syst Appl 40(8):3196–3206CrossRefGoogle Scholar
  73. 73.
    Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210CrossRefGoogle Scholar
  74. 74.
    Mezura-Montes E, López-Dávila EA (2012) Adaptation and local search in the modified bacterial foraging algorithm for constrained optimization. In: 2012 IEEE congress on evolutionary computation, IEEE, pp 1–8Google Scholar
  75. 75.
    Mirjalili S (2015a) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRefGoogle Scholar
  76. 76.
    Mirjalili S (2015b) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRefGoogle Scholar
  77. 77.
    Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073CrossRefGoogle Scholar
  78. 78.
    Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584CrossRefGoogle Scholar
  79. 79.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRefGoogle Scholar
  80. 80.
    Montgomery DC (2001) Design and analysis of experiments, 5th edn. Wiley, New DelhiGoogle Scholar
  81. 81.
    Musilek P, Krömer P, Bartoň T (2015) Review of nature-inspired methods for wake-up scheduling in wireless sensor networks. Swarm Evol Comput 25:100–118CrossRefGoogle Scholar
  82. 82.
    Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18CrossRefGoogle Scholar
  83. 83.
    Nápoles G, Grau I, Bello M, Bello R (2014) Towards swarm diversity: random sampling in variable neighborhoods procedure using a Lévy distribution. Computación y Sistemas 18(1):79–95Google Scholar
  84. 84.
    Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24CrossRefGoogle Scholar
  85. 85.
    Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670CrossRefGoogle Scholar
  86. 86.
    Olivas F, Valdez F, Castillo O (2015) Ant colony optimization with parameter adaptation using fuzzy logic for tsp problems. Design of intelligent systems based on fuzzy logic. Neural networks and nature-inspired optimization. Springer, Berlin, pp 593–603Google Scholar
  87. 87.
    Olivas F, Valdez F, Castillo O, Melin P (2016) Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic. Soft Comput 20(3):1057–1070CrossRefGoogle Scholar
  88. 88.
    Olorunda O, Engelbrecht AP (2008) Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). IEEE, pp 1128–1134Google Scholar
  89. 89.
    Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67MathSciNetCrossRefGoogle Scholar
  90. 90.
    Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844MathSciNetzbMATHCrossRefGoogle Scholar
  91. 91.
    Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2011) The bees algorithm—a novel tool for complex optimisation. In: Intelligent production machines and systems-2nd I* PROMS virtual international conference, Elsevier, p 454Google Scholar
  92. 92.
    Pham DT, Castellani M (2009) The bees algorithm: modelling foraging behaviour to solve continuous optimization problems. Proc Inst Mech Eng Part C J Mech Eng Sci 223(12):2919–2938CrossRefGoogle Scholar
  93. 93.
    Pham DT, Soroka AJ, Ghanbarzadeh A, Koc E, Otri S, Packianather M (2006) Optimising neural networks for identification of wood defects using the bees algorithm. In: 2006 4th IEEE international conference on industrial informatics. IEEE, pp 1346–1351Google Scholar
  94. 94.
    Pham Q (2007) Using statistical analysis to tune an evolutionary algorithm for dynamic optimization with progressive step reduction. Comput Chem Eng 31(11):1475–1483CrossRefGoogle Scholar
  95. 95.
    Pham QT, Pham DT, Castellani M (2012) A modified bees algorithm and a statistics-based method for tuning its parameters. Proc Inst Mech Eng Part I J Syst Control Eng 226(3):287–301CrossRefGoogle Scholar
  96. 96.
    Pluhacek M, Senkerik R, Davendra D, Oplatkova ZK, Zelinka I (2013a) On the behavior and performance of chaos driven pso algorithm with inertia weight. Comput Math Appl 66(2):122–134MathSciNetCrossRefGoogle Scholar
  97. 97.
    Pluhacek M, Senkerik R, Zelinka I, Davendra D (2013b) Chaos PSO algorithm driven alternately by two different chaotic maps-an initial study. In: IEEE congress on evolutionary computation, pp 2444–2449Google Scholar
  98. 98.
    Pluhacek M, Senkerik R, Zelinka I (2014) Particle swarm optimization algorithm driven by multichaotic number generator. Soft Comput 18(4):631–639CrossRefGoogle Scholar
  99. 99.
    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  100. 100.
    Pornsing C, Sodhi MS, Lamond BF (2016) Novel self-adaptive particle swarm optimization methods. Soft Comput 20(9):3579–3593CrossRefGoogle Scholar
  101. 101.
    Potter MA, Jong KAD (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29CrossRefGoogle Scholar
  102. 102.
    Richer TJ, Blackwell TM (2006) The Lévy particle swarm. In: IEEE congress on evolutionary computation, CEC 2006. IEEE, pp 808–815Google Scholar
  103. 103.
    Ruz GA, Goles E (2013) Learning gene regulatory networks using the bees algorithm. Neural Comput Appl 22(1):63–70CrossRefGoogle Scholar
  104. 104.
    Şahin E (2004) Swarm robotics: from sources of inspiration to domains of application. International workshop on swarm robotics. Springer, Berlin, pp 10–20Google Scholar
  105. 105.
    Sajja PS, Akerkar R (2013) Bio-inspired models for semantic web. Swarm intelligence and bio-inspired computation: theory and applications. Elsevier, Wlatham, pp 273–294CrossRefGoogle Scholar
  106. 106.
    Sanyal N, Chatterjee A, Munshi S (2011) An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation. Expert Syst Appl 38(12):15489–15498CrossRefGoogle Scholar
  107. 107.
    Schrijver A (2000) A course in combinatorial optimization. TU DelftGoogle Scholar
  108. 108.
    Senanayake M, Senthooran I, Barca JC, Chung H, Kamruzzaman J, Murshed M (2016) Search and tracking algorithms for swarms of robots: a survey. Robot Auton Syst 75:422–434CrossRefGoogle Scholar
  109. 109.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE World Congress on Computational Intelligence. IEEE, pp 69–73Google Scholar
  110. 110.
    Shi Y, Eberhart R (2001) Particle swarm optimization with fuzzy adaptive inertia weight. In: Proceedings of the workshop on particle swarm optimization, vol 1. Purdue School of Engineering and Technology, pp 101–106Google Scholar
  111. 111.
    Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 3. IEEE, pp 1945–1950Google Scholar
  112. 112.
    Sörensen K (2015) Metaheuristics the metaphor exposed. Int Trans Oper Res 22(1):3–18MathSciNetzbMATHCrossRefGoogle Scholar
  113. 113.
    Stützle T, López-Ibánez M, Pellegrini P, Maur M, De Oca MM, Birattari M, Dorigo M (2011) Parameter adaptation in ant colony optimization. Autonomous search. Springer, Berlin, pp 191–215CrossRefGoogle Scholar
  114. 114.
    Suganthan PN (1999) Particle swarm optimiser with neighbourhood operator. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, vol 3. IEEE, pp 1958–1962Google Scholar
  115. 115.
    Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Congress on evolutionary computation, CEC2004, vol 1. IEEE, pp 325–331Google Scholar
  116. 116.
    Sun J, Xu W, Feng B (2005) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: 2005 IEEE international conference on systems, man and cybernetics, vol 4. IEEE, pp 3049–3054Google Scholar
  117. 117.
    Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, New YorkzbMATHCrossRefGoogle Scholar
  118. 118.
    Tang D, Dai M, Salido MA, Giret A (2016) Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Comput Ind 81:82–95. CrossRefGoogle Scholar
  119. 119.
    Tang K, Yang P, Yao X (2016) Negatively correlated search. IEEE J Sel Areas Commun 34(3):542–550. CrossRefGoogle Scholar
  120. 120.
    Tanweer M, Suresh S, Sundararajan N (2016) Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems. Inf Sci 326:1–24. CrossRefGoogle Scholar
  121. 121.
    Thangeda P, Bhattacharya AK, Gopal R, Kumar RA (2018) Synthesis of optimal trajectories in aerial engagements using differential evolution. IFAC-PapersOnLine 51(1):90–97. CrossRefGoogle Scholar
  122. 122.
    Tian J, Tan Y, Zeng J, Sun C, Jin Y (2018) Multi-objective infill criterion driven gaussian process assisted particle swarm optimization of high-dimensional expensive problems. IEEE Trans Evol Comput. CrossRefGoogle Scholar
  123. 123.
    Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325MathSciNetzbMATHCrossRefGoogle Scholar
  124. 124.
    Tripathi PK, Bandyopadhyay S, Pal SK, (2007) Adaptive multi-objective particle swarm optimization algorithm. In: IEEE congress on evolutionary computation, CEC 2007. IEEE, pp 2281–2288Google Scholar
  125. 125.
    Tsai HC (2014) Novel bees algorithm: stochastic self-adaptive neighborhood. Appl Math Comput 247:1161–1172MathSciNetzbMATHGoogle Scholar
  126. 126.
    Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRefGoogle Scholar
  127. 127.
    Wang G, Chu HE, Zhang Y, Chen H, Hu W, Li Y, Peng X (2015) Multiple parameter control for ant colony optimization applied to feature selection problem. Neural Comput Appl 26(7):1693–1708CrossRefGoogle Scholar
  128. 128.
    Wang Y, Li B, Weise T, Wang J, Yuan B, Tian Q (2011) Self-adaptive learning based particle swarm optimization. Inf Sci 181(20):4515–4538MathSciNetzbMATHCrossRefGoogle Scholar
  129. 129.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  130. 130.
    Wu Q, Zhu Z, Yan X, Gong W (2018) An improved particle swarm optimization algorithm for avo elastic parameter inversion problem. Concurr Comput Pract Exp, p e4987Google Scholar
  131. 131.
    Wu Y, Liu G, Guo X, Shi Y, Xie L (2017) A self-adaptive chaos and kalman filter-based particle swarm optimization for economic dispatch problem. Soft Comput 21(12):3353–3365zbMATHCrossRefGoogle Scholar
  132. 132.
    Xu G (2013) An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput 219(9):4560–4569MathSciNetzbMATHGoogle Scholar
  133. 133.
    Yamaguchi T, Yasuda K (2006) Adaptive particle swarm optimization; self-coordinating mechanism with updating information. In: IEEE international conference on systems, man and cybernetics, SMC’06, vol 3. IEEE, pp 2303–2308Google Scholar
  134. 134.
    Yan X, Zhu Y, Zhang H, Chen H, Niu B (2012) An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discret Dyn Nat Soc 2012:1–20zbMATHGoogle Scholar
  135. 135.
    Yang P, Lu G, Tang K, Yao X (2016) A multi-modal optimization approach to single path planning for unmanned aerial vehicle. In: 2016 IEEE congress on evolutionary computation (CEC), IEEE, pp 1735–1742Google Scholar
  136. 136.
    Yang P, Tang K, Yao X (2018) Turning high-dimensional optimization into computationally expensive optimization. IEEE Trans Evol Comput 22(1):143–156CrossRefGoogle Scholar
  137. 137.
    Yang Q, Chen WN, Yu Z, Gu T, Li Y, Zhang H, Zhang J (2017) Adaptive multimodal continuous ant colony optimization. IEEE Trans Evol Comput 21(2):191–205CrossRefGoogle Scholar
  138. 138.
    Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, FromeGoogle Scholar
  139. 139.
    Yang XS (2010) Firefly algorithm, Levy flights and global optimization. Research and development in intelligent systems XXVI. Springer, Berlin, pp 209–218CrossRefGoogle Scholar
  140. 140.
    Yang XS (2012) Efficiency analysis of swarm intelligence and randomization techniques. J Comput Theoret Nanosci 9(2):189–198CrossRefGoogle Scholar
  141. 141.
    Yang XS, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspir Comput 5(3):141–149CrossRefGoogle Scholar
  142. 142.
    Yasuda T, Ohkura K, Matsumura Y (2010) Extended PSO with partial randomization for large scale multimodal problems. In: World automation congress (WAC), 2010, IEEE, pp 1–6Google Scholar
  143. 143.
    Zhan ZH, Zhang J, Li Y, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B (Cybernetics) 39(6):1362–1381CrossRefGoogle Scholar
  144. 144.
    Zheng F, Zecchin A, Newman J, Maier H, Dandy G (2017) An adaptive convergence-trajectory controlled ant colony optimization algorithm with application to water distribution system design problems. IEEE Trans Evol Comput 21(5):773–791CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of ITMonash UniversityClaytonAustralia
  2. 2.School of IT, Faculty of Science, Engineering and Built EnvironmentDeakin UniversityBurwoodAustralia

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