Skip to main content
Springer Nature Link
Log in

Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper proposes an improved social spider optimization (ISSO) for achieving different objectives of optimal reactive power dispatch (ORPD). The proposed ISSO method is developed by applying two modifications on new solution generation process. The proposed method uses only one modified equation for producing the first new solution generation and the second new solution generation while the standard SSO uses two equations for each process. The improvement in the proposed method is confirmed by solving benchmark optimization functions, IEEE 30-bus system and IEEE 118-bus system. Obtained results from ISSO are compared to those from other existing methods available in other studies together with other popular and state-of-the-art methods, which are implemented in the work. As compared to standard SSO for application to ORPD problem, ISSO can reduce the number of computation steps and one control parameter, and shorten simulation time. About the result comparisons with SSO and other remaining methods, ISSO can find more favorable solutions with higher quality and ISSO can stabilize solution search function with approximately all trial runs finding lower value of fitness. Furthermore, the strong search ability of ISSO is also indicated because it uses less value for control parameters. As a result, the proposed ISSO method can be a very effective optimization tool for dealing with the ORPD problem.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

HI:

The highest iteration

N bus :

Number of buses in the considered power system

N c :

Number of VAR compensator buses

Nfs, Nms :

Number of females and males, respectively

N G :

Number of generator buses

N line :

Number of transmission lines

N load :

Number of load buses in the considered power system

N pop :

Population size or the sum of males and females

N t :

Number of buses with transformer

Pdi, Qdi :

Real and reactive power required by load of bus i

P m :

Movement probability of female spiders

Q ci :

Reactive power generation of VAR compensator of bus i

Qci,min, Qci,max :

Lower and upper limitations of reactive power generation of VAR compensator at bus i

QGi,min, QGi,max :

Lower and upper limitations of reactive power generation of generator at bus i, respectively

RNf :

Random number within 0 and 1 for female spider f

S l,max :

Maximum apparent power flow of line l

Ti,min, Ti,max :

Lower and upper limitations of tap changer of transformer at bus i

VGi,min, VGi,max :

Lower and upper limitations of voltage magnitude of generator at bus i, respectively

Vi, Vj :

Voltage magnitude of buses i and j

Vloadi,min, Vloadi,max :

Lower and upper limitations of voltage magnitude of the load at bus i, respectively

X fs,f :

Position of female spider f corresponding to a solution

X Gbest :

Position of the best spider corresponding to the best solution

X ms,m :

Position of male spider m corresponding to a solution

ABCA:

Artificial bee colony algorithm

ACO:

Ant colony optimization

AGA:

Adaptive genetic algorithm

ALO:

Ant lion optimizer

ASCSA:

Adaptive selective cuckoo search algorithm

BA:

Bat algorithm

BB–BCA:

Big bang–big crunch algorithm

BBDE:

Bare-bones differential evolution

BBPSO:

Bare-bones particle swarm optimization

BOFs:

Benchmark optimization functions

BRCFA:

Binary real-coded firefly algorithm

BTSA:

Backtracking search algorithm

CABC-DE:

Hybrid chaotic artificial bee colony differential evolution

CKHA:

Chaotic krill herd algorithm

CLPSO:

Comprehensive learning particle swarm optimization

COA:

Coyote optimization algorithm

CSA:

Cuckoo search algorithm

CSSA:

Charged system search algorithm

DE:

Differential evolution

DE–AS:

Differential evolution and ant system

DPM:

Dynamic programming method

DSA:

Differential search algorithm

EMA:

Exchange market algorithm

EP:

Evolution programming

FA:

Firefly algorithm

FPA:

Flower pollination algorithm

GBBWCA:

Gaussian bare-bones water cycle algorithm

GBTLBO:

Gaussian bare-bones teaching learning-based optimization

GSA:

Gravitational search algorithm

GSA-CSS:

Gravitational search algorithm with original selection

GSA-NHCM:

Gravitational search algorithm with new constraint handling method

GWO:

Gray wolf optimizer

GWPSO:

Particle swarm optimization with inertia weight

HFA-NMS:

Hybrid Nelder–Mead simplex-based firefly algorithm

HFVNS:

Hybrid stochastic fractal search and variable neighborhood search

HICTS:

Hybrid imperialist competitive algorithm and tabu search

HISGA:

Hybrid interior search algorithm and genetic algorithm

HKAGA:

Hybrid Keshtel algorithm and genetic algorithm

HKASA:

Hybrid Keshtel algorithm and simulated annealing

HLGA:

Hybrid loop genetic algorithm

HMA:

Hybrid metaheuristic algorithm

HMPSO:

Hybrid multi-agent particle swarm optimization

HPSO–ICA:

Hybrid particle swarm optimization and imperialist competitive technique method

HPSO–TS:

Hybrid particle swarm optimization and tabu search

HRDSA:

Hybrid red deer algorithm and simulated annealing

HSA:

Harmony search algorithm

HSFSA:

Hybrid stochastic fractal search and simulated annealing

HSSSA:

Hybrid salp swarm algorithm and simulated annealing

HWPSO:

Hybrid whale optimization algorithm and particle swarm optimization

HWWGA:

Hybrid water wave optimizer and genetic algorithm

ICBO:

Improved colliding bodies optimization

IDA:

Improved deterministic algorithm

IMA:

Improved metaheuristic algorithm

IPG-PSO:

Improved pseudo-gradient search-particle swarm optimization

IPM:

Interior point method

IQP:

Improved quadratic programming

ISA:

Interior search algorithm

ISSO:

Improved social spider optimization

JA:

Jaya algorithm

KA:

Keshtel algorithm

LCA:

League championship algorithm

LDGWPSO:

Particle swarm optimization with linearly decreasing inertia weight

LPM:

Linear programming approach

MDE:

Modified differential evolution

MFO:

Moth flame optimization

MLPM:

Mixed-integer linear programming

MNM:

Modified Newton method

MORDA:

Multi-objective red deer algorithm

MPSO:

Modified particle swarm optimization

MSSA:

Modified salp swarm algorithm

MSSO:

Modified social spider optimization

MTLT–DDE:

Modified teaching learning technique and double differential evolution algorithm

NMSFLA:

Shuffled frog leaping algorithm and Nelder–Mead

ORCSA:

One rank cuckoo search algorithm

PG-PSO:

Particle swarm optimization with pseudo-gradient search

PGSWT-PSO:

Particle swarm optimization with stochastic weight trade-off and pseudo-gradient search

PSO:

Particle swarm optimization

PSO-ALC:

Particle swarm optimization with an aging leader and challengers

PSO-CF:

Particle swarm optimization with constriction factor

PSO-GT:

Particle swarm optimization with graph theory

PSO-TVAC:

Particle swarm optimization with time-varying acceleration coefficients

PSO-TVIW:

Particle swarm optimization with time-varying inertia weight

QODE:

Quasi-oppositional differential evolution

QOTLBO:

Quasi-oppositional teaching learning-based optimization

RCGA:

Real-coded genetic algorithm

RDA:

Red deer algorithm

RSGA:

Genetic algorithm with rank selection technique

SARCGA:

Self-adaptive real-coded genetic algorithm

SEO:

Social engineering optimizer

SFOA:

Sunflower optimization algorithm

SFS:

Stochastic fractal search

SGA:

Specialized genetic algorithm

SPSO-TVAC:

Particle swarm optimization with time-varying acceleration coefficients

SPSO-TVAC:

Particle swarm optimization with self-organization and time-varying acceleration coefficients

SSA:

Salp swarm algorithm

SSO:

Social spider optimization

SWT-PSO:

Particle swarm optimization with stochastic weight trade-off

TVAC:

Time-varying acceleration coefficients

WOA:

Whale optimization algorithm

WWO:

Water wave optimizer

References

  1. Haroon SS, Hassan S, Amin S, Sajjad IA, Waqar A, Aamir M, Alam I (2018) Multiple fuel machines power economic dispatch using stud differential evolution. Energies 11(6):1393. https://doi.org/10.3390/en11061393

    Article  Google Scholar 

  2. Nguyen TT, Vo DN, Vu QN, Van DL (2018) Modified Cuckoo search algorithm: a novel method to minimize the fuel cost. Energies 11(6):1328. https://doi.org/10.3390/en11061328

    Article  Google Scholar 

  3. Chintam JR, Daniel M (2018) Real-power rescheduling of generators for congestion management using a novel satin bowerbird optimization algorithm. Energies 11(1):183. https://doi.org/10.3390/en11010183

    Article  Google Scholar 

  4. Chen G, Lu Z, Zhang Z (2018) Improved krill herd algorithm with novel constraint handling method for solving optimal power flow problems. Energies 11(1):76. https://doi.org/10.3390/en11010076

    Article  Google Scholar 

  5. Da Costa GRM (2002) Modified Newton method for reactive dispatching. Int J Electr Power Energy Syst 24(10):815–819. https://doi.org/10.1016/S0142-0615(02)00013-3

    Article  Google Scholar 

  6. Deeb NI, Shahidehpour SM (1988) An efficient technique for reactive power dispatch using a revised linear programming approach. Electr Power Syst Res 15(2):121–134. https://doi.org/10.1016/0378-7796(88)90016-8

    Article  Google Scholar 

  7. Aoki K, Fan M, Nishikori A (1988) Optimal VAR planning by approximation method for recursive mixed-integer linear programming. IEEE Trans Power Syst 3(4):1741–1747. https://doi.org/10.1109/59.192990

    Article  Google Scholar 

  8. Granville S (1994) Optimal reactive dispatch through interior point methods. IEEE Trans Power Syst 9(1):136–146. https://doi.org/10.1109/59.317548

    Article  Google Scholar 

  9. Rezania E, Shahidehpour SM (2001) Real power loss minimization using interior point method. Int J Electr Power Energy Syst 23(1):45–56. https://doi.org/10.1016/S0142-0615(00)00028-4

    Article  Google Scholar 

  10. Lu FC, Hsu YY (1995) Reactive power/voltage control in a distribution substation using dynamic programming. IEE Proc Gener Transm Distrib 142(6):639–645. https://doi.org/10.1049/ip-gtd:19952210

    Article  Google Scholar 

  11. Grudinin N (1998) Reactive power optimization using successive quadratic programming method. IEEE Trans Power Syst 13(4):1219–1225. https://doi.org/10.1109/59.736232

    Article  Google Scholar 

  12. Shunmugalatha A, Slochanal SMR (2008) Application of hybrid multiagent-based particle swarm optimization to optimal reactive power dispatch. Electr Power Compon Syst 36(8):788–800. https://doi.org/10.1080/15325000801911385

    Article  Google Scholar 

  13. Mahadevan K, Kannan PS (2010) Comprehensive learning particle swarm optimization for reactive power dispatch. Appl Soft Comput 10(2):641–652. https://doi.org/10.1016/j.asoc.2009.08.038

    Article  Google Scholar 

  14. Sahli Z, Hamouda A, Bekrar A, Trentesaux D (2018) Reactive power dispatch optimization with voltage profile improvement using an efficient hybrid algorithm. Energies 11(8):2134. https://doi.org/10.3390/en11082134

    Article  Google Scholar 

  15. Singh RP, Mukherjee V, Ghoshal SP (2015) Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Appl Soft Comput 29:298–309. https://doi.org/10.1016/j.asoc.2015.01.006

    Article  Google Scholar 

  16. Reddy PL, Yesuratnam G (2015) PSO based optimal reactive power dispatch for voltage profile improvement. In: Power, communication and information technology conference (PCITC), 2015 IEEE, pp 361–366. https://doi.org/10.1109/PCITC.2015.7438192

  17. Polprasert J, Ongsakul W, Dieu VN (2016) Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electr Power Compon Syst 44(5):518–532. https://doi.org/10.1080/15325008.2015.1112449

    Article  Google Scholar 

  18. Medani KBO, Sayah S (2016) Optimal reactive power dispatch using particle swarm optimization with time varying acceleration coefficients. In: Modelling, identification and control (ICMIC), 2016 8th international conference on IEEE, pp 780–785. https://doi.org/10.1109/ICMIC.2016.7804219

  19. Mehdinejad M, Mohammadi-Ivatloo B, Dadashzadeh-Bonab R, Zare K (2016) Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms. Int J Electr Power Energy Syst 83:104–116. https://doi.org/10.1016/j.ijepes.2016.03.039

    Article  Google Scholar 

  20. Rayudu K, Yesuratnam G, Ali M, Jayalaxmi A (2016) Optimal reactive power dispatch based on particle swarm optimization and LP technique. In: Emerging technological trends (ICETT), international conference on IEEE, pp 1–7. https://doi.org/10.1109/ICETT.2016.7873777

  21. Kaur D, Lie TT, Nair NK, Valles B (2016) An optimal reactive power dispatch (ORPD) for voltage security using particle swarm optimization (PSO) in graph theory. In Sustainable energy technologies (ICSET), 2016 IEEE international conference on IEEE, pp 25–30. https://doi.org/10.1109/ICSET.2016.7811751

  22. El Ela AA, Abido MA, Spea SR (2011) Differential evolution algorithm for optimal reactive power dispatch. Electr Power Syst Res 81(2):458–464. https://doi.org/10.1016/j.epsr.2010.10.005

    Article  Google Scholar 

  23. Huang CM, Huang YC (2012) Combined differential evolution algorithm and ant system for optimal reactive power dispatch. Energy Proc 14:1238–1243. https://doi.org/10.1016/j.egypro.2011.12.1082

    Article  Google Scholar 

  24. Ghasemi M, Ghanbarian MM, Ghavidel S, Rahmani S, Moghaddam EM (2014) Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: a comparative study. Inf Sci 278:231–249. https://doi.org/10.1016/j.ins.2014.03.050

    Article  MathSciNet  Google Scholar 

  25. Basu M (2016) Quasi-oppositional differential evolution for optimal reactive power dispatch. Int J Electr Power Energy Syst 78:29–40. https://doi.org/10.1016/j.ijepes.2015.11.067

    Article  Google Scholar 

  26. Li Y, Li X, Li Z (2017) Reactive power optimization using hybrid CABC-DE algorithm. Electr Power Compon Syst 45(9):980–989. https://doi.org/10.1080/15325008.2017.1311387

    Article  Google Scholar 

  27. Cao YJ, Wu QH (1997) Optimal reactive power dispatch using an adaptive genetic algorithm. In: Genetic algorithms in engineering systems: innovations and applications. GALESIA 97. Second international conference on IET, pp 117–122. https://doi.org/10.1049/cp:19971166

  28. Subbaraj P, Rajnarayanan PN (2009) Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr Power Syst Res 79(2):374–381. https://doi.org/10.1016/j.epsr.2008.07.008

    Article  Google Scholar 

  29. Alam MS, De M (2016) Optimal reactive power dispatch using hybrid loop-genetic based algorithm. In: Power systems conference (NPSC) IEEE, pp 1–6. https://doi.org/10.1109/NPSC.2016.7858901

  30. Villa-Acevedo W, López-Lezama J, Valencia-Velásquez J (2018) A novel constraint handling approach for the optimal reactive power dispatch problem. Energies 11(9):2352. https://doi.org/10.3390/en11092352

    Article  Google Scholar 

  31. Văduva AM, Bulac C (2016) New evolutionary algorithm method for solving optimal reactive power dispatch problem. In: Applied and theoretical electricity (ICATE), 2016 international conference on IEEE, pp 1–6. https://doi.org/10.1109/ICATE.2016.7754626

  32. Duman S, Sönmez Y, Güvenç U, Yörükeren N (2012) Optimal reactive power dispatch using a gravitational search algorithm. IET Gener Transm Distrib 6(6):563–576. https://doi.org/10.1049/iet-gtd.2011.0681

    Article  Google Scholar 

  33. Roy PK, Mandal B, Bhattacharya K (2012) Gravitational search algorithm based optimal reactive power dispatch for voltage stability enhancement. Electr Power Compon Syst 40(9):956–976. https://doi.org/10.1080/15325008.2012.675405

    Article  Google Scholar 

  34. Babu MR, Lakshmi M (2016) Gravitational search algorithm based approach for optimal reactive power dispatch. In: Science technology engineering and management (ICONSTEM), Second international conference on IEEE, pp 360–366. https://doi.org/10.1109/ICONSTEM.2016.7560977

  35. Chen G, Liu L, Zhang Z, Huang S (2017) Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints. Appl Soft Comput 50:58–70. https://doi.org/10.1016/j.asoc.2016.11.008

    Article  Google Scholar 

  36. El-Ela AA, Kinawy AM, El-Sehiemy RA, Mouwafi MT (2011) Optimal reactive power dispatch using ant colony optimization algorithm. Electr Eng 93(2):103–116. https://doi.org/10.1007/s00202-011-0196-4

    Article  Google Scholar 

  37. Rayudu K, Yesuratnam G, Jayalaxmi A (2017) Ant colony optimization algorithm based optimal reactive power dispatch to improve voltage stability. In: Circuit, power and computing technologies (ICCPCT), 2017 international conference on IEEE, pp 1–6. https://doi.org/10.1109/ICCPCT.2017.8074391

  38. Mandal B, Roy PK (2013) Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. Int J Electr Power Energy Syst 53:123–134. https://doi.org/10.1016/j.ijepes.2013.04.011

    Article  Google Scholar 

  39. Ghasemi M, Taghizadeh M, Ghavidel S, Aghaei J, Abbasian A (2015) Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm. Eng Appl Artif Intell 39:100–108. https://doi.org/10.1016/j.engappai.2014.12.001

    Article  Google Scholar 

  40. Sulaiman MH, Mustaffa Z (2017) Cuckoo search algorithm as an optimizer for optimal reactive power dispatch problems. In: Control, automation and robotics (ICCAR), 2017 3rd international conference on IEEE, pp 735–739. https://doi.org/10.1109/ICCAR.2017.7942794

  41. An NHT, Dieu VN, Nguyen TT, Kien VT (2015) One rank cuckoo search algorithm for optimal reactive power dispatch. GMSARN Int J 9:73–82

    Google Scholar 

  42. Khazali AH, Kalantar M (2011) Optimal reactive power dispatch based on harmony search algorithm. Int J Electr Power Energy Syst 33(3):684–692. https://doi.org/10.1016/j.ijepes.2010.11.018

    Article  Google Scholar 

  43. Khorsandi A, Alimardani A, Vahidi B, Hosseinian SH (2011) Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch. IET Gener Transm Distrib 5(2):249–256. https://doi.org/10.1049/iet-gtd.2010.0256

    Article  Google Scholar 

  44. Mukherjee A, Mukherjee V (2015) Solution of optimal reactive power dispatch by chaotic krill herd algorithm. IET Gener Transm Distrib 9(15):2351–2362. https://doi.org/10.1049/iet-gtd.2015.0077

    Article  Google Scholar 

  45. Rajan A, Malakar T (2015) Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. Int J Electr Power Energy Syst 66:9–24. https://doi.org/10.1016/j.ijepes.2014.10.041

    Article  Google Scholar 

  46. Sulaiman MH, Mustaffa Z, Mohamed MR, Aliman O (2015) Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl Soft Comput 32:286–292. https://doi.org/10.1016/j.asoc.2015.03.041

    Article  Google Scholar 

  47. Rayudu K, Yesuratnam G, Jayalaxmi A (2016) Artificial bee colony algorithm for optimal reactive power dispatch to improve voltage stability. In: Circuit, power and computing technologies (ICCPCT), 2016 international conference on IEEE, pp 1–7. https://doi.org/10.1109/ICCPCT.2016.7530203

  48. Rajan A, Malakar T (2016) Exchange market algorithm based optimum reactive power dispatch. Appl Soft Comput 43:320–336. https://doi.org/10.1016/j.asoc.2016.02.041

    Article  Google Scholar 

  49. Abaci K, Yamaçli V (2017) Optimal reactive-power dispatch using differential search algorithm. Electr Eng 99(1):213–225. https://doi.org/10.1007/s00202-016-0410-5

    Article  Google Scholar 

  50. Mouassa S, Bouktir T, Salhi A (2017) Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng Sci Technol Int J 3(20):885–895. https://doi.org/10.1016/j.jestch.2017.03.006

    Article  Google Scholar 

  51. Shaheen AM, El-Sehiemy RA, Farrag SM (2016) Optimal reactive power dispatch using backtracking search algorithm. Aust J Electr Electron Eng 13(3):200–210. https://doi.org/10.1080/1448837X.2017.1325134

    Article  Google Scholar 

  52. Anbarasan P, Jayabarathi T (2017) Optimal reactive power dispatch problem solved by an improved colliding bodies optimization algorithm. In: Environment and electrical engineering and 2017 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe), 2017 IEEE international conference on IEEE, pp 1–6. https://doi.org/10.1109/EEEIC.2017.7977592

  53. Heidari AA, Abbaspour RA, Jordehi AR (2017) Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl Soft Comput 57:657–671. https://doi.org/10.1016/j.asoc.2017.04.048

    Article  Google Scholar 

  54. Mandal S, Mandal KK, Kumar S (2017) A new optimization technique for optimal reactive power scheduling using Jaya algorithm. In: Power and advanced computing technologies (i-PACT), 2017 innovations in IEEE, pp 1–5. https://doi.org/10.1109/IPACT.2017.8244961

  55. Kennedy J, Eberhart R (1995) Particle swarm optimizer. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1945. https://doi.org/10.1109/ICNN.1995.488968

  56. Yang XS. (2010). A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization NICSO 2010. Springer, Berlin, pp 65–74. https://doi.org/10.1007/978-3-642-12538-6_6

  57. Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, pp 111–127

  58. Yang XS, Deb S (2009) Cuckoo search via Levy lights. In: World congress on nature and biologically inspired computing, NaBIC, pp 210–214. https://doi.org/10.1109/NABIC.2009.5393690

  59. Yang XS (2012) Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249. https://doi.org/10.1007/978-3-642-32894-7_27

  60. Cuevas E, Cienfuegos M, Zaldívar D, Pérez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40:6374–6384. https://doi.org/10.1016/j.eswa.2013.05.041

    Article  Google Scholar 

  61. Hajiaghaei-Keshteli M, Aminnayeri M (2013) Keshtel Algorithm (KA); a new Optimizer algorithm inspired by Keshtels’ feeding. In: Proceedings of the IEEE conference on industrial engineering management system, pp 2249–2253

  62. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimizer. ISA Trans 53:1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018

    Article  Google Scholar 

  63. Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11. https://doi.org/10.1016/j.cor.2014.10.008

    Article  MathSciNet  MATH  Google Scholar 

  64. Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18. https://doi.org/10.1016/j.knosys.2014.07.025

    Article  Google Scholar 

  65. Mirjalili S, Lewis A (2016) The whale optimizer algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  66. Fathollahi-Fard AM, Hajiaghaei-Keshteli M (2016) Red deer algorithm (RDA) a new optimization algorithm inspired by red deer’s mating. In: Proceedings of international conference on industrial engineering, IEEE pp 33–34

  67. Elsayed WT, Hegazy YG, Bendary FM, El-bages MS (2016) Modified social spider algorithm for solving the economic dispatch problem. Eng Sci Technol Int J 4(19):1672–1681. https://doi.org/10.1016/j.jestch.2016.09.002

    Article  Google Scholar 

  68. Sun SC, Qi H, Ren YT, Yu XY, Ruan LM (2017) Improved social spider optimization algorithms for solving inverse radiation and coupled radiation-conduction heat transfer problems. Int Commun Heat Mass Transfer 87:132–146. https://doi.org/10.1016/j.icheatmasstransfer.2017.07.010

    Article  Google Scholar 

  69. El Aziz MA, Hassanien AE (2017) An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2804-8

    Article  Google Scholar 

  70. Pierezan J, Coelho LDS (2018) Coyote optimization algorithm: a new metaheuristic for global optimization problems. In: 2018 IEEE congress on evolutionary computation (CEC), IEEE, pp 1–8. https://doi.org/10.1109/CEC.2018.8477769

  71. Gomes GF, da Cunha SS, Ancelotti AC (2018) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Engineering with Computers. https://doi.org/10.1007/s00366-018-0620-8

    Article  Google Scholar 

  72. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2018) The social engineering optimizer (SEO). Eng Appl Artif Intell 72:267–293. https://doi.org/10.1016/j.engappai.2018.04.009

    Article  Google Scholar 

  73. Fathollahi-Fard AM, Hajiaghaei-Keshteli M (2018) A bi-objective partial interdiction problem considering different defensive systems with capacity expansion of facilities under imminent attacks. Appl Soft Comput 68:343–359. https://doi.org/10.1016/j.asoc.2018.04.011

    Article  Google Scholar 

  74. Hajiaghaei-Keshteli M, Fathollahi-Fard AM (2018) A set of efficient heuristics and metaheuristics to solve a two-stage stochastic bi-level decision-making model for the distribution network problem. Comput Ind Eng 123:378–395. https://doi.org/10.1016/j.cie.2018.07.009

    Article  Google Scholar 

  75. Samadi A, Mehranfar N, Fathollahi-Fard AM, Hajiaghaei-Keshteli M (2018) Heuristic-based metaheuristics to address a sustainable supply chain network design problem. J Ind Prod Eng 35(2):102–117. https://doi.org/10.1080/21681015.2017.1422039

    Article  Google Scholar 

  76. Hajiaghaei-Keshteli M, Fathollahi-Fard AM (2018) Sustainable closed-loop supply chain network design with discount supposition. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3369-5

    Article  Google Scholar 

  77. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Mirjalili S (2018) Hybrid optimizers to solve a tri-level programming model for a tire closed-loop supply chain network design problem. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2018.06.021

    Article  Google Scholar 

  78. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2017.07.00

    Article  Google Scholar 

  79. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R (2018) A bi-objective green home health care routing problem. J Clean Prod 200:423–443. https://doi.org/10.1016/j.jclepro.2018.07.258

    Article  Google Scholar 

  80. Nguyen TT, Dieu VN, Dinh BH (2018) An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems. Energy 155:930–956. https://doi.org/10.1016/j.energy.2018.05.037

    Article  Google Scholar 

  81. Kang T, Yao J, Jin M, Yang S, Duong T (2018) A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies 11(5):1–31. https://doi.org/10.3390/en11051060

    Article  Google Scholar 

  82. Fayaz M, Kim D (2018) Energy consumption optimization and user comfort management in residential buildings using a bat algorithm and fuzzy logic. Energies 11(1):161. https://doi.org/10.3390/en11010161

    Article  Google Scholar 

  83. Vo DN, Schegner P (2013) An improved particle swarm optimization for optimal power flow. In: Vasant PM (ed) Meta-heuristics optimization algorithms in engineering, business, economics, and finance. Pennsylvania, IGI Global, pp 1–40. https://doi.org/10.4018/978-1-4666-2086-5.ch001

    Chapter  Google Scholar 

  84. Xiong G, Shi D (2018) Orthogonal learning competitive swarm optimizer for economic dispatch problems. Appl Soft Comput 66:134–148. https://doi.org/10.1016/j.asoc.2018.02.019

    Article  Google Scholar 

  85. MATPOWER 4.1 IEEE 30-bus and 118-bus test system. http://www.pserc.cornell.edu/matpower. Accessed 1 Jan 2018

  86. Mernik M, Liu SH, Karaboga D, Crepinšek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inf Sci 29:115–127. https://doi.org/10.1016/j.ins.2014.08.040

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thang Trung Nguyen

  2. Department of Power Systems, Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Dieu Ngoc Vo

Authors
  1. Thang Trung Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dieu Ngoc Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dieu Ngoc Vo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 14, 15, 16 and 17.

Table 14 Optimal solutions obtained by the proposed method for Case 3
Full size table
Table 15 Optimal solution obtained by ISSO for the IEEE 118-bus power system with minimization of power loss
Full size table
Table 16 Optimal solution obtained by ISSO for the IEEE 118-bus power system with minimization of voltage deviation
Full size table
Table 17 Optimal solution obtained by ISSO for the IEEE 118-bus power system with minimization of L index
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, T.T., Vo, D.N. Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives. Neural Comput & Applic 32, 5919–5950 (2020). https://doi.org/10.1007/s00521-019-04073-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04073-4

Keywords