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Soft Computing

, Volume 20, Issue 2, pp 727–747 | Cite as

ODMA: a novel swarm-evolutionary metaheuristic optimizer inspired by open source development model and communities

  • Hossein Hajipour
  • Hamed Behzadi Khormuji
  • Habib Rostami
Methodologies and Application

Abstract

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by \(17\) well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence.

Keywords

Metaheuristic search Optimization  Evolutionary computation Swarm intelligence optimization 

References

  1. Ali MZ, Reynolds RG (2014) Cultural algorithms: a tabu search approach for the optimization of engineering design problems. Soft Comput. 18(8):1631–1644CrossRefGoogle Scholar
  2. Andrews DF, Mallows CL (1974) Scale mixtures of normal distributions. J R Stat Soc. Ser B (Methodol) 36(1):99–102. doi: 10.2307/2984774 zbMATHMathSciNetGoogle Scholar
  3. Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Evolutionary Programming VII. Springer, pp 601–610Google Scholar
  4. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation. IEEE, pp 4661–4667Google Scholar
  5. Bajpai P, Singh S (2007) Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market. Power Syst, IEEE Trans 22(4):2152–2160. doi: 10.1109/TPWRS.2007.907445 CrossRefGoogle Scholar
  6. Baojiang Z, Shiyong L (2007) Ant colony optimization algorithm and its application to neuro-fuzzy controller design. Syst Eng Electron, J 18(3):603–610. doi: 10.1016/S1004-4132(07)60135-2 zbMATHCrossRefGoogle Scholar
  7. Becerra RL, Coello CAC (2006) Cultured differential evolution for constrained optimization. Comput Methods Appl Mech Eng 195(33–36):4303–4322zbMATHCrossRefGoogle Scholar
  8. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308CrossRefGoogle Scholar
  9. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: nsga-ii. Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  10. Dennis JE, More JJ (1977) Quasi-Newton methods, motivation and theory. SIAM Rev 19(1):46–89Google Scholar
  11. DiBona C, Ockman S, Stone M (eds) Open sources. Voices from the open source revolution, 1st edn. O’Reilly, SebastopolGoogle Scholar
  12. Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77(4):481–506Google Scholar
  13. Distribution-Watch: Distrowatch. http://distrowatch.com (2014)
  14. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst, Man, Cybern, Part B 26(1):29–41CrossRefGoogle Scholar
  15. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278zbMATHMathSciNetCrossRefGoogle Scholar
  16. dos Santos Coelho L, Souza RCT, Mariani VC (2009) Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems. Math Comput Simul 79(10):3136–3147zbMATHCrossRefGoogle Scholar
  17. Eberhart R, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 congress on evolutionary computation, 2001, vol 1. pp 81–86. doi: 10.1109/CEC.2001.934374
  18. Elamvazuthi I, Vasant P, Ganesan T (2013) Hybrid optimization techniques for optimization in a fuzzy environment. In: Zelinka I, Snásel V, Abraham A (eds) Handbook of optimization, intelligent systems reference library, vol 38. Springer, New York, pp 1025–1046Google Scholar
  19. Ellabib I, Calamai PH, Basir OA (2007) Exchange strategies for multiple ant colony system. Inf Sci 177(5):1248–1264CrossRefGoogle Scholar
  20. Ganesan T, Vasant P, Elamvazuthi I (2013) Hybrid neuro-swarm optimization approach for design of distributed generation power systems. Neural Comput Appl 23(1):105–117CrossRefGoogle Scholar
  21. Gargari EA (2014) ICA Website http://www.icasite.info/
  22. Gen M, Syarif A (2005) Hybrid genetic algorithm for multi-time period production/distribution planning. Comput Ind Eng 48(4):799–809CrossRefGoogle Scholar
  23. Ghosh RA, Glott R, Krieger B, Robles G (2002) Free/libre and open source software: survey and study (floss) part 4: survey of developers. Tech. rep., International Institute of Infonomics, University of Maastricht. http://www.infonomics.nl/FLOSS/report/
  24. Gill PE, Murray W, Wright MH (1981) Practical optimization. Academic Press Inc. [Harcourt Brace Jovanovich Publishers], London.Google Scholar
  25. GNU-Linux: Gnu/linux distribution timeline. http://futurist.se/gldt/ (2014)
  26. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., BostonzbMATHGoogle Scholar
  27. Gómez J, Gil C, Baños R, Márquez A, Montoya F, Montoya M (2013) A pareto-based multi-objective evolutionary algorithm for automatic rule generation in network intrusion detection systems. Soft Comput 17(2):255–263CrossRefGoogle Scholar
  28. Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evolut Comput 11(1):1–18CrossRefGoogle Scholar
  29. Hansen N, Auger A, Finck S, Ros R et al (2010) Real-parameter black-box optimization benchmarking 2010: experimental setup. Technical report (2010)Google Scholar
  30. Hassan R, Cohanim BE, de Weck OL (2005) Comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, AIAA-2005-1897. American Institute of Aeronautics and Astronautics, Austin, Texas (2005)Google Scholar
  31. Haupt RL, Haupt SE (1998) Practical genetic algorithms. John Wiley & Sons Inc, New YorkzbMATHGoogle Scholar
  32. Hui Zhan Z, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans. Evol Comput 15(6), 832–847Google Scholar
  33. Jiménez F, Sánchez G, Juárez JM (2014) Multi-objective evolutionary algorithms for fuzzy classification in survival prediction. Artif Intell Med 60(3):197–219CrossRefGoogle Scholar
  34. Juang CF (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. Syst Man Cybern Part B 34(2):997–1006CrossRefGoogle Scholar
  35. Kao YT, Zahara E (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8(2):849–857CrossRefGoogle Scholar
  36. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948Google Scholar
  37. Kronfeld M, Planatscher H, Zell A (2010) The eva2 optimization framework. In: Blum C, Battiti R (eds) LION, Lecture notes in computer science, vol 6073. Springer, New York, pp 247–250Google Scholar
  38. Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Technical reportGoogle Scholar
  39. 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
  40. Li X, Tang K, Omidvar MN, Yang Z, Qin K (2013) Benchmark functions for the cec’2013 special session and competition on large-scale global optimization. Gene 7:33Google Scholar
  41. Liu G, Guo W, Niu Y, Chen G, Huang X (2014) A pso-based timing-driven octilinear steiner tree algorithm for vlsi routing considering bend reduction. Soft Comput 1–17. doi: 10.1007/s00500-014-1329-2
  42. Mahil J, Raja TSR (2014) An intelligent biological inspired evolutionary algorithm for the suppression of incubator interference in premature infants ecg. Soft Comput 18(3):571–578CrossRefGoogle Scholar
  43. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210CrossRefGoogle Scholar
  44. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  45. Molga M, Smutnicki C (2005) Test functions for optimization needs. Technical report, Bioinformatics LaboratoryGoogle Scholar
  46. Muffatto: Open Source: a multidisciplinary approach, 1 edn. World Scientific Publishing (2006)Google Scholar
  47. Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms:a comparative study. IEEE Trans Syst Man Cybern-Part B 36(1) (2006)Google Scholar
  48. Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Dover Publications, MineolaGoogle Scholar
  49. Pelta DA, Krasnogor N (2009) Nature-inspired cooperative strategies for optimization. Int J Intell Syst 24(7):723–725CrossRefGoogle Scholar
  50. Puchinger J, Raidl GR (2005) Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification. In: Mira J, Álvarez JR (eds) IWINAC (2), vol 3562. Springer, New York, pp 41–53Google Scholar
  51. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248zbMATHCrossRefGoogle Scholar
  52. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255CrossRefGoogle Scholar
  53. Raymond ES (1999) The cathedral and the bazaar, 1st edn. O’Reilly & Associates Inc, SebastopolGoogle Scholar
  54. Reynolds RG (1999) New ideas in optimization. chap. Cultural algorithms: theory and applications, McGraw-Hill Ltd., Maidenhead, pp 367–378Google Scholar
  55. Robinson J, Sinton S, Rahmat-Samii Y (2002) Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: Antennas and propagation society international symposium, 2002. IEEE, vol 1, pp 314–317. IEEEGoogle Scholar
  56. Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3):284–294CrossRefGoogle Scholar
  57. Schwefel HP (1995) Evolution and optimum seeking, Sixth-generation computer technology series. Wiley, New YorkGoogle Scholar
  58. Settles M, Soule T (2005) Breeding swarms: a ga/pso hybrid. In: Proceedings of the 2005 conference on genetic and evolutionary computation, ACM, pp 161–168Google Scholar
  59. Shi Y, Eberhart R (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99, vol 3Google Scholar
  60. Stallman R (1984) The free software definition (1984). Available at: http://www.gnu.org/philosophy/free-sw.html
  61. Storn R, Price K (1997) Differential evolution– a simple and efficient heuristic for global optimization over continuous spaces. J. Glob Optim 11(4):341–359zbMATHMathSciNetCrossRefGoogle Scholar
  62. Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325zbMATHMathSciNetCrossRefGoogle Scholar
  63. van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937–971zbMATHCrossRefGoogle Scholar
  64. Volk W (1969) Applied statistics for engineers. McGraw-Hill, New YorkGoogle Scholar
  65. Williams S (2002) Free as in freedom. Richard Stallman’s crusade for free software. O’Reilly, Beijing; Cambridge; Farnham; Köln (2002)Google Scholar
  66. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. Trans Evol Comp 1(1):67–82. doi: 10.1109/4235.585893
  67. Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999Google Scholar
  68. Yao X (1993) An empirical study of genetic operators in genetic algorithms. Microprocess Microprogr 31(2):707–714Google Scholar
  69. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102CrossRefGoogle Scholar
  70. Zhiqiang G, Huaiqing W, Quan L (2013) Financial time series forecasting using lpp and svm optimized by pso. Soft Comput 17(5):805–818CrossRefGoogle Scholar
  71. Zuo X, Xiao L (2014) A de and pso based hybrid algorithm for dynamic optimization problems. Soft Comput 18(7), 1405–1424 (2014). doi: 10.1007/s00500-013-1153-0. http://dx.doi.org/10.1007/s00500-013-1153-0

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hossein Hajipour
    • 1
  • Hamed Behzadi Khormuji
    • 1
  • Habib Rostami
    • 1
  1. 1.Computer Engineering Department, School of EngineeringPersian Gaulf University of BushehrBushehrIran

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