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Neural Computing and Applications

, Volume 23, Issue 2, pp 429–454 | Cite as

Swallow swarm optimization algorithm: a new method to optimization

  • Mehdi Neshat
  • Ghodrat Sepidnam
  • Mehdi Sargolzaei
Original Article

Abstract

This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.

Keywords

Computational intelligence Swallow swarm optimization (SSO) Benchmark function Fish swarm optimization Particle swarm optimization 

References

  1. 1.
    Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New YorkMATHGoogle Scholar
  2. 2.
    Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41CrossRefGoogle Scholar
  3. 3.
    Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
  4. 4.
    Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, CambridgeMATHCrossRefGoogle Scholar
  5. 5.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks. IEEE Press, Piscataway, pp 1942–1948CrossRefGoogle Scholar
  6. 6.
    Clerc M (2007) Particle swarm optimization. ISTE Ltd., LondonGoogle Scholar
  7. 7.
    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  8. 8.
    Li XL (2003) A new intelligent optimization-artificial fish swarm algorithm. PhD thesis, Zhejiang University, China, June, 2003Google Scholar
  9. 9.
    Jiang MY, Yuan DF (2006) Artificial fish swarm algorithm and its applications. In: Proceedings of the international conference on sensing, computing and automation, (ICSCA’2006). Chongqing, China, 8–11 May. 2006, pp 1782–1787Google Scholar
  10. 10.
    Xiao JM, Zheng XM, Wang XH (2006) A modified artificial fish-swarm algorithm. In Proc. of the IEEE 6th World Congress on Intelligent Control and Automation, (WCICA’2006). Dalian, China, 21–23 June 2006, pp 3456–3460Google Scholar
  11. 11.
    Krishnanand KN, Ghose D (2005) Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. Proceedings of IEEE swarm intelligence symposium. IEEE Press, Piscataway, pp 84–91Google Scholar
  12. 12.
    Krishnanand KN, Ghose D (2006) Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst 2(3):209–222MATHGoogle Scholar
  13. 13.
    Krishnanand KN, Ghose D (2006) Theoretical foundations for multiple rendezvous of glowworm inspired mobile agents with variable local-decision domains. Proceedings of American control conference. IEEE Press, Piscataway, pp 3588–3593Google Scholar
  14. 14.
    Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3:87–124. doi: 10.1007/s11721-008-0021-5 Google Scholar
  15. 15.
    Dorigo M, Trianni V, Sahin E, Gross R, Labella TH, Baldassarre G, Nolfi S, Deneubourg J-L, Mondada F, Floreano D, Gambardella LM (2004) Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots 17(2–3):223–245CrossRefGoogle Scholar
  16. 16.
    Fronczek JW, Prasad NR (2005) Bio-inspired sensor swarms to detect leaks in pressurized systems. In: Proceedings of IEEE international conference on systems, man and cybernetics. IEEE Press, Piscataway, pp 1967–1972Google Scholar
  17. 17.
    Zarzhitsky D, Spears DF, Spears WM (2005) Swarms for chemical plume tracing. Proceedings of IEEE Swarm intelligence symposium. IEEE Press, Piscataway, pp 249–256Google Scholar
  18. 18.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353Google Scholar
  19. 19.
    Heppner H, Grenander U (1990) A stochastic non-linear model for coordinated bird flocks. In: Krasner S (ed) The ubiquity of chaos. AAAS, Washington, pp 233–238Google Scholar
  20. 20.
    Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science. IEEE, Nagoya, Japan, Piscataway, pp 39–43Google Scholar
  21. 21.
    Eberhart RC, Simpson PK, Dobbins RW (1996) Computational intelligence PC tools. Academic Press, BostonGoogle Scholar
  22. 22.
    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization an overview. Swarm Intell 1:33–57. doi: 10.1007/s11721-007-0002-0 Google Scholar
  23. 23.
    Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE world congress on computational intelligence, pp 69–73Google Scholar
  24. 24.
    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
  25. 25.
    Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325MathSciNetMATHCrossRefGoogle Scholar
  26. 26.
    Yasuda K, Ide A, Iwasaki N (2003) Stability analysis of particle swarm optimization. In: Proceedings of the 5th metaheuristics international conference, pp. 341–346Google Scholar
  27. 27.
    Kadirkamanathan V, Selvarajah K, Fleming PJ (2006) Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Trans Evol Comput 10(3):245–255CrossRefGoogle Scholar
  28. 28.
    van den Bergh F, Engelbrecht AP (2006) A study of particle optimization particle trajectories. Inf Sci 176(8):937–971MATHCrossRefGoogle Scholar
  29. 29.
    Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of IEEE congress on evolution and computation, pp 1945–1950Google Scholar
  30. 30.
    Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. IEEE Congr Evol Comput 1:101–106Google Scholar
  31. 31.
    Eberhart RC, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of IEEE congress on evolution and computation, Seoul, Korea, pp 94–97Google Scholar
  32. 32.
    Clerc M (1999) The swarm and the queen: toward a deterministic and adaptive particle swarm optimization. In: Proceedings of IEEE Congress on Evolution and Computation, pp 1951–1957Google Scholar
  33. 33.
    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
  34. 34.
    Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceeding of IEEE Congress on Evolution and Computation, pp 84–88Google Scholar
  35. 35.
    Kennedy J (1997) The particle swarm social adaptation of knowledge. In: Proceedings of IEEE international conference on Evolution and computation. Indianapolis, IN, pp 303–308Google Scholar
  36. 36.
    Suganthan PN (1999) Particle swarm optimizer with neighborhood operator. In: Proceedings of IEEE congress on evolution and computation. Washington DC, pp 1958–1962Google Scholar
  37. 37.
    Ratnaweera A, Halgamuge S, Watson H (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255CrossRefGoogle Scholar
  38. 38.
    Angeline PJ (1998) Using selection to improve particle swarm optimization. In: Proceedings of IEEE congress on evolution and computation. Anchorage, AK, pp 84–89Google Scholar
  39. 39.
    Juang CF (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B Cybern 34(2):997–1006CrossRefGoogle Scholar
  40. 40.
    Chen YP, Peng WC, Jian MC (2007) Particle swarm optimization with recombination and dynamic linkage discovery. IEEE Trans Syst Man Cybern B Cybern 37(6):1460–1470Google Scholar
  41. 41.
    Andrews PS (2006) An investigation into mutation operators for particle swarm optimization. In: Proceedings of IEEE congress on evolution and computation. Vancouver, BC, Canada, pp 1044–1051Google Scholar
  42. 42.
    Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. In: Proceedings of IEEE congress on evolution and computation, pp 522–528Google Scholar
  43. 43.
    Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: Proceedings of IEEE conference on systems, man, cybernetics, pp 3816–3821Google Scholar
  44. 44.
    van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRefGoogle Scholar
  45. 45.
    Ratnaweera A, Halgamuge S, Watson H (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255CrossRefGoogle Scholar
  46. 46.
    Parsopoulos KE, Vrahatis MN (2004) On the computation of all global minimizers through particle swarm optimization. IEEE Trans Evol Comput 8(3):211–224MathSciNetCrossRefGoogle Scholar
  47. 47.
    Brits R, Engelbrecht AP, van den Bergh F (2002) A niching particle swarm optimizer. In: Proceedings of 4th Asia-Pacific conference on simulation and evolution and learning, pp. 692–696Google Scholar
  48. 48.
    Brits R, Engelbrecht AP, van den Bergh F (2007) Locating multiple optima using particle swarm optimization. Appl Math Comput 189(2):1859–1883MathSciNetMATHCrossRefGoogle Scholar
  49. 49.
    Parrott D, Li XD (2006) Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans Evol Comput 10(4):440–458CrossRefGoogle Scholar
  50. 50.
    Zhan Z, Zhang J, Li Y, Shu-Hung Chung H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 39(6):1362–1381CrossRefGoogle Scholar
  51. 51.
    Liu J-L, Chang C–C (2008) Novel orthogonal momentum-type particle swarm optimization applied to solve large parameter optimization problems. J Artif Evol Appl 1:1–9MathSciNetCrossRefGoogle Scholar
  52. 52.
    Sivanandam SN, Visalakshi P (2009) Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimization. Int J Bio Inspired Comput 1(4):276–286CrossRefGoogle Scholar
  53. 53.
    Zhan Z-H, Zhang J, Li Y, Shi Y-H (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847 Google Scholar
  54. 54.
    Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of IEEE congress on evolution and computation. Honolulu, HI, pp 1671–1676Google Scholar
  55. 55.
    Kennedy J, Mendes R (2006) Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans Syst Man Cyber Part C Appl Rev 36(4):515–519CrossRefGoogle Scholar
  56. 56.
    Hu X, Eberhart RC (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of IEEE congress on evolution and computation. Honolulu, HI, pp 1677–1681Google Scholar
  57. 57.
    Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings of swarm intelligence symposium, pp 124–129Google Scholar
  58. 58.
    Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: Simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210CrossRefGoogle Scholar
  59. 59.
    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
  60. 60.
    Li LX, Shao ZJ, Qian JX (2002) An Optimizing method based on autonomous animals: fish-swarm algorithm. Syst Eng Theory Pract 22(11):32–38Google Scholar
  61. 61.
    Zhang M, Shao C, Li F, Gan Y, Sun J (2006) Evolving neural network classifiers and feature subset using artificial fish swarm. In: Proceedings of the 2006 IEEE international conference on mechatronics and automation, June 25–28. Luoyang, ChinaGoogle Scholar
  62. 62.
    Jiang M, Wang Y, Rubio F, Yuan D (2007) Spread spectrum code estimation by artificial fish swarm algorithm. In: IEEE international symposium on intelligent signal processing (WISP)Google Scholar
  63. 63.
    Jiang MY, Yuan DF (2005) Wavelet threshold optimization with artificial fish swarm algorithm. In: Proceedings of the IEEE international conference on neural networks and brain, (ICNN&B’2005), Beijing, China, 13–15, pp 569–572Google Scholar
  64. 64.
    Paul Gorenzel W, Salmon TP (1994) Swallows, prevention and control of wildlife damageGoogle Scholar
  65. 65.
    Lazareck LJ, Moussavi Z Adaptive swallowing sound segmentation by variance dimensionGoogle Scholar
  66. 66.
    Angela T, Chris R (1989) Swallows and martins: an identification guide and handbook. Houghton-Mifflin. ISBN 0-395-51174-7Google Scholar
  67. 67.
    Bijlsma RG, van den Brink B (2005) A Barn Swallow Hirundo rustica roost under attack:timing and risks in the presence of African Hobbies Falco cuvieri. Ardea 93(1):37–48Google Scholar
  68. 68.
    Saino N, Galeotti P, Sacchi R, Møller A (1997) Song and immunological condition in male barn swallows (Hirundo rustica). Behav Ecol 8(94):364–371. doi: 10.1093/beheco/8.4.364 (http://dx.doi.org/10.1093%2Fbeheco%2F8.4.364)
  69. 69.
    Brown CR (1986) Cliff swallow colonies as information centers. Science 234:83–85Google Scholar
  70. 70.
    Brown CR, Brown M, Shaffer ML (1991) food sharing signals among socially foraging cliff swallows. Anim Behav 42:551–564CrossRefGoogle Scholar
  71. 71.
    Safran R (2010) Barn swallows: sexual and social behavior. Encycl Animal Behav 1:139–144 (Elsevier)Google Scholar
  72. 72.
    Snapp BD (1976) Colonial breeding in the barn swallow (hirundo rustica) and its adaptive significance. Condor 783471480Google Scholar
  73. 73.
    Smith LC, Raouf SA, Brown MB, Wingfield JC, Brown CR (2005) Testosterone and group size in cliff swallows: testing the “challenge hypothesis” in a colonial bird. Horm Behav 47:76–82CrossRefGoogle Scholar
  74. 74.
    Mccarty JP, Winkler DW (1999) Foraging ecology and diet tree swallows feeding selectivity of nestlings. The Condor IO 1:246–254. The cooper ornithological societyGoogle Scholar
  75. 75.
    Whitley D, Rana D, Dzubera J, Mathias E (1996) Evaluating evolutionary algorithms. Artif Intell 85(1–2):245–276CrossRefGoogle Scholar
  76. 76.
    Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems 39:263–278CrossRefGoogle Scholar
  77. 77.
    Esquivel SC, Coello CAC (2003) On the use of particle swarm optimization with multimodal functions. IEEE Congr Evol Comput 2:1130–1136Google Scholar
  78. 78.
    Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wily, New YorkGoogle Scholar
  79. 79.
    Esmin AAA, Lambert-Torres G, Alvarenga GB (2006) UFLA, Brazil, hybrid evolutionary algorithm based on PSO and GA mutation, sixth international conference on hybrid intelligent systems. HIS ‘06Google Scholar
  80. 80.
    Settles M, Soule T (2005) Breeding swarms: A GA/PSO Hybrid. In: GECCO ‘05: proceedings of the 2005 conference on genetic and evolutionary computation, pp 161–168Google Scholar
  81. 81.
    Meng Y, Kazeem O (2007) A hybrid ACO/PSO control algorithm for distributed swarm robots. In: Proceedings of the 2007 IEEE swarm intelligence symposium (SIS 2007)Google Scholar
  82. 82.
    Gomez-Cabrero D, Ranasinghe DN (2005) Fine-tuning the ant colony system algorithm through particle swarm optimization, technical report TR07-2005. Departamento de Estadistica e Investigacio Operativa, Universitat de Valencia, Burjassot, SpainGoogle Scholar
  83. 83.
    Chen H, Wang S, Li J, Li Y (2007) A hybrid of artificial fish swarm algorithm and particle swarm optimization for feed forward neural network training, 2007 international conference on intelligent systems and knowledge engineering (ISKE 2007)Google Scholar
  84. 84.
    Shi H, Bei Z (2008) Application of improved ant colony algorithm. In: 4th International conference on natural computation. ICNC ‘08Google Scholar
  85. 85.
    Shi H, Bei Z (2009) A mixed ant colony algorithm for function optimization. In: Proceedings of the 21st annual international conference on Chinese control and decision IEEE Press Piscataway, NJ, USA, pp 3919–3923Google Scholar
  86. 86.
    Mishra SK (2006) Performance of differential evolution and particle swarm methods on some relatively harder multi-modal benchmark functions. Available at SSRN: http://ssrn.com/abstract=937147
  87. 87.
    Ho S-Y, Lin H-S, Liauh W-H, Ho S-J (2008) OPSO: Orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans Syst Man Cybern Part A 38(2):288–298Google Scholar
  88. 88.
    Berliner S (2004) The Birders Report. http://home.earthlink.net/~s.berliner/

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Mehdi Neshat
    • 1
  • Ghodrat Sepidnam
    • 1
  • Mehdi Sargolzaei
    • 1
  1. 1.Department of Computer EngineeringShirvan Branch, Islamic Azad UniversityShirvanIran

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