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A review on constraint handling strategies in particle swarm optimisation

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Abstract

Almost all real-world optimisation problems are constrained. Solving constrained problems is difficult for optimisation techniques. In this paper, different constraint handling strategies used in heuristic optimisation algorithms and especially particle swarm optimisation (PSO) are reviewed. Since PSO is a very common optimisation algorithm, this paper can provide a broad view to researchers in related field and help them to identify the appropriate constraint handling strategy for their own optimisation problem.

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References

  1. Jordehi AR, Joorabian M (2011) Optimal placement of multi-type FACTS devices in power systems using evolution strategies. In: Power engineering and optimization conference (PEOCO), 2011 5th International, IEEE, 2011, pp 352–357

  2. Jordehi AR, Jasni J (2011) A comprehensive review on methods for solving FACTS optimization problem in power systems. Int Rev Electr Eng 6:1916–1926

    Google Scholar 

  3. Jordehi AR (2014) A chaotic-based big bang-big crunch algorithm for solving global optimisation problems. Neural Comput Appl 25:1329–1335. doi:10.1007/s00521-014-1613-1

    Article  Google Scholar 

  4. Jordehi AR (2014) Optimal setting of TCSC’s in power systems using teaching-learning-based optimisation algorithm. Neural Comput Appl. doi:10.1007/s00521-014-1791-x

    Google Scholar 

  5. Jordehi AR (2014) A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems. Neural Comput Appl. doi:10.1007/s00521-014-1751-5

    Google Scholar 

  6. Jordehi AR (2014) Chaotic bat swarm optimisation (CBSO). Appl Soft Comput 26:523–530. doi:10.1016/j.asoc.2014.10.010

    Article  Google Scholar 

  7. Beheshti Z, Hj Shamsuddin SM (2014) CAPSO: centripetal accelerated particle swarm optimization. Inf Sci 258:54–79

    Article  Google Scholar 

  8. Ahandani MA, Alavi-Rad H (2015) Opposition-based learning in shuffled frog leaping: an application for parameter identification. Inf Sci 291:19–42. doi:10.1016/j.ins.2014.08.031

    Article  Google Scholar 

  9. Wang H, Zhao G, Li N (2012) Training support vector data descriptors using converging linear particle swarm optimization. Neural Comput Appl 21:1099–1105

    Article  Google Scholar 

  10. Li X, Zhang J, Yin M (2013) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24:1867–1877. doi:10.1007/s00521-013-1433-8

    Article  Google Scholar 

  11. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64:55–61

    Article  Google Scholar 

  12. Yildiz AR (2013) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13:1561–1566

    Article  Google Scholar 

  13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. Perth, Australia, pp 1942–1948

  14. Jordehi AR (2014), Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput 26:401–417

    Article  Google Scholar 

  15. Jordehi AR, Jasni J (2013) Parameter selection in particle swarm optimisation: a survey. J Exp Theor Artif Intell 25:527–542

    Article  Google Scholar 

  16. Jordehi AR, Jasni J (2012) Approaches for FACTS optimization problem in power systems. In: Power engineering and optimization conference (PEDCO) Melaka, Malaysia, 2012 Ieee International, IEEE, 2012, pp 355–360

  17. Jordehi R (2011) Heuristic methods for solution of FACTS optimization problem in power systems. In: 2011 IEEE student conference on research and development, pp 30–35

  18. 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 12:171–195

    Article  Google Scholar 

  19. Jordehi AR, Jasni J (2013) Particle swarm optimisation for discrete optimisation problems: a review. Artif Intell Rev 1–16. doi:10.1007/s10462-012-9373-8

  20. Jordehi AR, Jasni J, Abdul Wahab NI, Kadir A, Abidin MZ (2013) Particle swarm optimisation applications in FACTS optimisation problem. In: Power engineering and optimization conference (PEOCO), 2013 IEEE 7th International, IEEE, pp 193–198

  21. Jordehi AR (2014) Particle swarm optimisation for dynamic optimisation problems: a review. Neural Comput Appl 25:1507–1516. doi:10.1007/s00521-014-1661-6

    Article  Google Scholar 

  22. Jordehi AR, Jasni J, Abd Wahab N, Kadir MZ, Javadi MS (2015) Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSC’s in power systems. Int J Electr Power Energy Syst 64:771–784

    Article  Google Scholar 

  23. Eberhart RC, Shi Y, Kennedy J (2001) Swarm intelligence. Elsevier, Amsterdam

    Google Scholar 

  24. Chong EK, Zak SH (2013) An introduction to optimization. Wiley, New York

    Google Scholar 

  25. Rao SS, Rao S (2009) Engineering optimization: theory and practice. Wiley, New York

    Book  Google Scholar 

  26. Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Taylor & Francis, London

    Book  Google Scholar 

  27. Homaifar A, Qi CX, Lai SH (1994) Constrained optimization via genetic algorithms. Simulation 62:242–253

    Article  Google Scholar 

  28. Morales AK, Quezada CV (1998) A universal eclectic genetic algorithm for constrained optimization. In: Proceedings of the 6th European congress on intelligent techniques and soft computing, vol 1, pp 518–522. http://cursos.itam.mx/akuri/PUBLICA.CNS/1998/Universal%20EGA%20%28EUFIT98%29.PDF

  29. Hoffmeister F, Sprave J (1996) Problem-independent handling of constraints by use of metric penalty functions. In: Proceedings of evolutionary programming, pp 289–294. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.5900

  30. Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s. In: IEEE, 1994, vol 572, pp 579–584

  31. Ben Hadj-Alouane A, Bean JC (1997) A genetic algorithm for the multiple-choice integer program. Oper Res 45:92–101

    Article  MathSciNet  Google Scholar 

  32. Carlson SE, Shonkwiler R (1998) Annealing a genetic algorithm over constraints. In: IEEE, 1998, vol 3934, pp 3931–3936

  33. Coello Coello CA (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127

    Article  Google Scholar 

  34. Michalewicz Z, Nazhiyath G (1995) Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints. In: IEEE, 1995, vol 642, pp 647–651

  35. Xiao J, Michalewicz Z, Zhang L, Trojanowski K (1997) Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans Evol Comput 1:18–28

    Article  Google Scholar 

  36. Surry P, Radcliffe N, Boyd I (1995) A multi-objective approach to constrained optimisation of gas supply networks: the COMOGA method. Evol Comput 993:166–180

    Article  Google Scholar 

  37. Paredis J (1994) Co-evolutionary constraint satisfaction. In: Parallel problem solving from nature—PPSN III, pp 46–55

  38. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338

    Article  Google Scholar 

  39. Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4:284–294

    Article  Google Scholar 

  40. Le TV (1995) A fuzzy evolutionary approach to constrained optimization problems. In: IEEE proceeding on evolutionary computation conference, pp 274–278. doi:10.1109/ICEC.1996.542374

  41. Parsopoulos K, Vrahatis M (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Advances in natural computation, vol 3612. Springer, Berlin, Heidelberg, pp 582–591

    Chapter  Google Scholar 

  42. Zheng J, Wu Q, Song W (2007) An improved particle swarm algorithm for solving nonlinear constrained optimization problems. In: IEEE, 2007, pp 112–117

  43. Saber AY, Ahmmed S, Alshareef A, Abdulwhab A, Adbullah-Al-Mamun K (2007) Constrained non-linear optimization by modified particle swarm optimization. In: IEEE, 2007, pp 1–7

  44. Li X, Tian P, Kong M (2005) A novel particle swarm optimization for constrained optimization problems. In: AI 2005: advances in artificial intelligence, (2005), pp 1305–1310

  45. Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problems. Intell Technol Theory Appl New Trends Intell Technol 76:214–220

    Google Scholar 

  46. Hu X, Eberhart RC, Shi Y (2003) Engineering optimization with particle swarm. In: IEEE, 2003, pp 53–57

  47. Hu X, Eberhart R (2002) Solving constrained nonlinear optimization problems with particle swarm optimization. In: Citeseer, 2002, pp 203–206

  48. He S, Prempain E, Wu Q (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36:585–605

    Article  MathSciNet  Google Scholar 

  49. Coath G, Halgamuge SK (2003) A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems. In: The 2003 congress on evolutionary computation, 2003. CEC ‘03, vol 2414, pp 2419–2425

  50. Flores-Mendoza J, Mezura-Montes E (2008) Looking inside particle swarm optimization in constrained search spaces. In: MICAI 2008: advances in artificial intelligence, pp 451–461

  51. Cagnina L, Esquivel S, Coello C (2006) A particle swarm optimizer for constrained numerical optimization. In: Parallel problem solving from nature-PPSN IX, pp 910–919

  52. He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1422

    Article  MathSciNet  Google Scholar 

  53. Sun CL, Zeng JC, Pan JS (2009) An improved particle swarm optimization with feasibility-based rules for constrained optimization problems. In: Next-generation applied intelligence, pp 202–211

  54. Zavala A, Aguirre A, Diharce E (2009) Continuous constrained optimization with dynamic tolerance using the COPSO algorithm. In: Constraint-handling in evolutionary optimization, pp 1–23

  55. Pulido GT, Coello CAC (2004) A constraint-handling mechanism for particle swarm optimization. In: Ieee, 2004, vol 1392, pp 1396–1403

  56. Cabrera JCF, Coello CAC (2007) Handling constraints in particle swarm optimization using a small population size. In: Springer, Berlin, pp 41–51

  57. Munoz-Zavala AE, Hernandez-Aguirre A, Villa-Diharce ER, Botello-Rionda S (2006) PESO+ for constrained optimization. In: IEEE congress on evolutionary computation, 2006. CEC 2006, pp 231–238

  58. Kou X, Liu S, Zhang J, Zheng W (2009) Co-evolutionary particle swarm optimization to solve constrained optimization problems. Comput Math Appl 57:1776–1784

    Article  Google Scholar 

  59. Worasucheep C (2008) Solving constrained engineering optimization problems by the constrained PSO-DD. In: IEEE, 2008, pp 5–8

  60. Liu H, Xu S, Liang X (2008) A modified quantum-behaved particle swarm optimization for constrained optimization. In: IEEE, 2008, pp 531–534

  61. Munoz Zavala AE, Aguirre AH, Villa Diharce ER (2005) Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: ACM, 2005, pp 209–216

  62. Lu H, Chen W (2006) Dynamic-objective particle swarm optimization for constrained optimization problems. J Comb Optim 12:409–419

    Article  MathSciNet  Google Scholar 

  63. Lu H, Chen W (2008) Self-adaptive velocity particle swarm optimization for solving constrained optimization problems. J Global Optim 41:427–445

    Article  MathSciNet  Google Scholar 

  64. Ray T, Liew K (2001), A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems. In: IEEE, 2001, vol 71, pp 75–80

  65. Li LD, Li X, Yu X (2008) Power generation loading optimization using a multi-objective constraint-handling method via PSO algorithm. In: IEEE, 2008, pp 1632–1637

  66. Krohling RA, dos Santos Coelho L (2006) Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern B Cybern 36:1407–1416

    Article  Google Scholar 

  67. Liang J, Suganthan P (2006), Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. In: IEEE, 2006, pp 9–16

  68. Jian L, Zhiming L, Peng C (2008) Solving constrained optimization via dual particle swarm optimization with stochastic ranking. In: Ieee, 2008, pp 1215–1218

  69. Takahama T, Sakai S (2004) Constrained optimization by combining the α constrained method with particle swarm optimization. In: Proceedings of joint 2nd international conference on soft computing and intelligent systems and 5th international symposium on advanced intelligent systems

  70. Takahama T, Sakai S (2005) Constrained optimization by applying the α constrained method to the nonlinear simplex method with mutations. IEEE Trans Evol Comput 9:437–451

    Article  Google Scholar 

  71. Takahama T, Sakai S (2006) Solving constrained optimization problems by the ε constrained particle swarm optimizer with adaptive velocity limit control. In: 2006 IEEE conference on cybernetics and intelligent systems, IEEE, 2006, pp 1–7

  72. Omeltschuk L, Helwig S, Muhlenthaler M, Wanka R (2011) Heterogeneous constraint handling for particle swarm optimization. In: 2011 IEEE symposium on swarm intelligence (SIS), IEEE, 2011, pp 1–7

  73. Helwig S, Wanka R (2007) Particle swarm optimization in high-dimensional bounded search spaces. In: Swarm intelligence symposium, 2007. SIS 2007. IEEE, IEEE, 2007, pp 198–205

  74. Sedlaczek K, Eberhard P (2006) Using augmented Lagrangian particle swarm optimization for constrained problems in engineering. Struct Multidiscip Optim 32:277–286

    Article  Google Scholar 

  75. Sedlaczek K, Eberhard P (2005), Constrained particle swarm optimization of mechanical systems. In: 6th world congresses of structural and multidisciplinary optimization Rio de Janeiro, vol 30

  76. Azadani EN, Hosseinian S, Moradzadeh B (2010) Generation and reserve dispatch in a competitive market using constrained particle swarm optimization. Int J Electr Power Energy Syst 32:79–86

    Article  Google Scholar 

  77. Daneshyari M, Yen GG (2012) Constrained multiple-swarm particle swarm optimization within a cultural framework. IEEE Trans Syst Man Cybern A Syst Hum 42:475–490

    Article  Google Scholar 

  78. Daneshyari M, Yen GG (2010) Solving constrained optimization using multiple swarm cultural PSO with inter-swarm communication. In: 2010 IEEE congress on evolutionary computation (CEC), IEEE, 2010, pp 1–8

  79. Del Valle YE (2009) Optimization of power system performance using facts devices. PhD thesis, Georgia Tech University. https://smartech.gatech.edu/bitstream/handle/1853/29612/delvalle_yamille_e_200908_phd.pdf

  80. del Valle Y, Digman M, Gray A, Perkel J, Venayagamoorthy GK, Harley RG (2008) Enhanced particle swarm optimizer for power system applications. In: Swarm intelligence symposium, 2008. SIS 2008. IEEE, IEEE, 2008, pp 1–7

  81. Wang J, Yin Z (2008) A ranking selection-based particle swarm optimizer for engineering design optimization problems. Struct Multidiscip Optim 37:131–147

    Article  Google Scholar 

  82. Leguizamón G, Coello Coello CA (2009) Boundary search for constrained numerical optimization problems with an algorithm inspired by the ant colony metaphor. IEEE Trans Evol Comput 13:350–368

    Article  Google Scholar 

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Jordehi, A.R. A review on constraint handling strategies in particle swarm optimisation. Neural Comput & Applic 26, 1265–1275 (2015). https://doi.org/10.1007/s00521-014-1808-5

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