Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison

  • Sanchari DebEmail author
  • Xiao-Zhi Gao
  • Kari Tammi
  • Karuna Kalita
  • Pinakeswar Mahanta
Original Paper


The escalated energy demand in conjunction with the global warming and environmental degradation has paved the path of transportation electrification. Electric Vehicles (EVs) need to recharge their batteries after travelling certain distance. Thus, large scale deployment of EVs calls for development of sustainable charging infrastructure. The placement of charging stations is a complex optimization problem involving a number of decision variables, objective functions, and constraints. Placement of charging station mimics a non-convex and non- combinatorial problem involving both transport and distribution network. The complex and non-linear nature of the charging station placement problem has compelled researchers to apply Nature Inspired Optimization (NIO) algorithms for solving the problem. This study aims to review the NIO algorithms applied for solving the charging station placement problem. This work will endow the research community with a systematic review of NIO algorithms for solving charging station placement problem thereby revealing the key features, advantages, and disadvantages of each of these algorithms. Thus, this work will help the researchers in selecting suitable algorithm for solving the charging station placement problem and will serve as a guide for developing efficient algorithms to solve the charging station placement problem.




Electric vehicle


Natire inspired optimization


Genetic algorithm


Differential evolution


Evolutionary strategy


Particle swarm optimization


Chicken swarm optimization


Bird swarm algorithm


Ant Colony optimization


Elephant herding optimization


Firefly algorithm


Grey wolf algorithm


Whale optimization algorithm


Cuckoo search


Flower pollination algorithm


Symbiotic organisms search


Simulated annealing


Lightning search algorithm


Gravitational search algorithm


Rain water algorithm


Harmony search


Teaching learning based optimization


Ying Yang pair algorithm


Spotted Hyena optimizer


No free lunch theorem


Total harmonic distortion

Decision Variables


Bus number where charging station is to be placed


Number of fast charging station at bus b


Number of slow charging station at bus b

Constant Parameters

nfast CS

Maximum number of fast charging stations that can be placed at a particular bus

nslow CS

Maximum number of fast charging stations that can be placed at a particular bus


Lower bound of reactive power limit of each bus


Upper bound of reactive power limit of each bus


Loading margin of the network


Base voltage


Total number of buses of the distribution network


Weight assigned to V


Weight assigned to R


Weight assigned to SAIFI


Weight assigned to SAIDI


Weight assigned to CAIDI


Weight assigned to Power loss


Base value of Voltage Stability Index


Base value of SAIFI


Base value of SAIDI


Base value of CAIDI


Base value of power loss



Installation cost of charging station


Operating cost of charging station


Penalty paid by utility


Travelling distance cost from point of charging station to point of placement of charging station


Voltage Deviation


Composite Reliability Index



Voltage of ith bus for base case


Voltage of ith bus after placement of charging station


Voltage Deviation of ith bus


Load at ith bus


Active power generation of ith bus


Active power demand of ith bus


Reactive power generation of ith bus


Reactive power demand of ith bus


Voltage of jth bus


Magnitude of (i,j)th term of bus admittance matrix


Angle of Yij


Voltage angle of ith bus


Voltage angle of jth bus


VSI after after the placement of EV charging stations


Power loss after the placement of EV charging stations


SAIFI after the placement of charging stations in the distribution network


SAIDI after the placement of charging stations in the distribution network


CAIDI after the placement of charging stations in the distribution network

NIO Parameters


Particle’s best position


Swarm’s best position


Total population


Set of roosters


Set of hens


Population of chicks


Set of mother hens




mean value of decision variable


Random number between 0 and 2


Maximum generation


positive constant to introduce the frequency of CSO


Current iteration count



The fund was provided by National Natural Science Foundation of China (Grand No. 51875113).

Compliance with Ethical Standards

Conflict of interest

We have no conflict of interest with this research article.

Human and Animal Rights

We use no animal in this research.


  1. 1.
    Deb S, Tammi K, Kalita K, Mahanta P (2018) Impact of electric vehicle charging station load on distribution network. Energies 11(1):178CrossRefGoogle Scholar
  2. 2.
    Clement N, Kristien EH, Johan D (2010) The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25(1):371–380CrossRefGoogle Scholar
  3. 3.
    Dubey A, Surya S (2015) Electric vehicle charging on residential distribution systems: impacts and mitigations. IEEE Access 3:1871–1893CrossRefGoogle Scholar
  4. 4.
    Deb S, Kalita K, Mahanta P (2017) Review of impact of electric vehicle charging station on the power grid. In: 2017 international conference on technological advancements in power and energy (TAP Energy). IEEEGoogle Scholar
  5. 5.
    Kongjeen Y, Bhumkittipich K (2018) Impact of plug-in electric vehicles integrated into power distribution system based on voltage-dependent power flow analysis. Energies 11(6):1–16CrossRefGoogle Scholar
  6. 6.
    Deb S, Kalita K, Mahanta P (2017) Impact of electric vehicle charging stations on reliability of distribution network. In: 2017 international conference on technological advancements in power and energy (TAP Energy). IEEEGoogle Scholar
  7. 7.
    Deb S, Tammi K, Kalita K, Mahanta P (2018) Review of recent trends in charging infrastructure planning for electric vehicles. In: Wiley Interdisciplinary Reviews: Energy and Environment, p e306Google Scholar
  8. 8.
    Rahman I, Vasant PM, Singh BSM, Abdullah-Al-Wadud M, Adnan N (2016) Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures. Renew Sustain Energy Rev 58:1039–1047CrossRefGoogle Scholar
  9. 9.
    Zhang H, Hu Z, Xu Z, Song Y (2017) Optimal planning of PEV charging station with single output multiple cables charging spots. IEEE Trans Smart Grid 8(5):2119–2128CrossRefGoogle Scholar
  10. 10.
    Zhang H, Moura SJ, Hu Z, Qi W, Song Y (2018) A second-order cone programming model for planning PEV fast-charging stations. IEEE Trans Power Syst 33(3):2763–2777CrossRefGoogle Scholar
  11. 11.
    Martínez-Lao J, Montoya FG, Montoya MG, Manzano-Agugliaro F (2017) Electric vehicles in Spain: an overview of charging systems. Renew Sustain Energy Rev 77:970–983CrossRefGoogle Scholar
  12. 12.
    Shareef H, Islam MM, Mohamed A (2016) A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles. Renew Sustain Energy Rev 64:403–420CrossRefGoogle Scholar
  13. 13.
    Islam MM, Shareef H, Mohamed A (2015) A review of techniques for optimal placement and sizing of electric vehicle charging stations. Przegląd Elektrotechniczny 8:122–126Google Scholar
  14. 14.
    Amjad M, Ahmad A, Rehmani MH, Umer T (2018) A review of EVs charging: from the perspective of energy optimization, optimization approaches, and charging techniques. Transp Res Part D Transp Environ 62:386–417CrossRefGoogle Scholar
  15. 15.
    Fister Jr I, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186
  16. 16.
    Dhal KG, Ray S, Das A, Das S (2019) A survey on nature-inspired optimization algorithms and their application in image enhancement domain. Arch Comput Methods Eng 26:1–32MathSciNetCrossRefGoogle Scholar
  17. 17.
    Vikhar PA (2016) Evolutionary algorithms: a critical review and its future prospects. In: International conference on global trends in signal processing, information computing and communication (ICGTSPICC), 2016, pp 261–265. IEEEGoogle Scholar
  18. 18.
    Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23CrossRefGoogle Scholar
  19. 19.
    Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99CrossRefGoogle Scholar
  20. 20.
    Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Rechenberg I (1978) Evolutionsstrategien. In: Simulationsmethoden in der Medizin und Biologie. Springer, Berlin, pp 83–114Google Scholar
  22. 22.
    Beyer HG, Schwefel HP (2002) Evolution strategies–a comprehensive introduction. Nat Comput 1(1):3–52MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS’95. IEEE, pp 39–43Google Scholar
  24. 24.
    Meng XB, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, Cham, pp 86–94Google Scholar
  25. 25.
    Meng XB, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird Swarm Algorithm. J Exp Theor Artif Intell 28(4):673–687CrossRefGoogle Scholar
  26. 26.
    Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. In: Toward a practice of autonomous systems: proceedings of the first European conference on artificial life. MIT Press, p 134Google Scholar
  27. 27.
    Wang GG, Deb S, Coelho LDS (2015) Elephant herding optimization. In: 3rd international symposium on computational and business intelligence (ISCBI), 2015, pp 1–5. IEEEGoogle Scholar
  28. 28.
    Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspir Comput 2(2):78–84CrossRefGoogle Scholar
  29. 29.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  30. 30.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRefGoogle Scholar
  31. 31.
    Yang XS, Deb S (2009) Cuckoo search via Le´vy flights. In: World Congress on Nature and Biologically Inspired Computing, 2009. NaBIC 2009. IEEE, pp 210–214Google Scholar
  32. 32.
    Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRefGoogle Scholar
  33. 33.
    Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249CrossRefGoogle Scholar
  34. 34.
    Dhiman G, Kaur A (2017) Spotted hyena optimizer for solving engineering design problems. In: International conference on machine learning and data science (MLDS), 2017, pp 114–119. IEEEGoogle Scholar
  35. 35.
    Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struc 139:98–112CrossRefGoogle Scholar
  36. 36.
    Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333CrossRefGoogle Scholar
  37. 37.
    Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248zbMATHCrossRefGoogle Scholar
  38. 38.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  40. 40.
    Biyanto TR, Syamsi MN, Fibrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JA, Malwindasari A, Abdillah AI, Bethiana TN, Putra YA (2017) Optimization of energy efficiency and conservation in green building design using duelist, killer-whale and rain-water algorithms. In: IOP conference series: materials science and engineering, vol 267, no. 1. IOP Publishing, p 012036Google Scholar
  41. 41.
    Rao RV, Kalyankar VD (2011) Parameters optimization of advanced machining processes using TLBO algorithm, vol 20. EPPM, SingaporeGoogle Scholar
  42. 42.
    Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34Google Scholar
  43. 43.
    Punnathanam V, Kotecha P (2016) Yin-Yang-pair optimization: a novel lightweight optimization algorithm. Eng Appl Artif Intell 54:62–79CrossRefGoogle Scholar
  44. 44.
    Abd-El-Wahed WF, Mousa AA, El-Shorbagy MA (2011) Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J Comput Appl Math 235(5):1446–1453MathSciNetzbMATHCrossRefGoogle Scholar
  45. 45.
    Deb S, Kalita K, Gao XZ, Tammi K, Mahanta P (2017) Optimal placement of charging stations using CSO-TLBO algorithm. In: Third international conference on research in computational intelligence and communication networks (ICRCICN), 2017, pp 84–89. IEEEGoogle Scholar
  46. 46.
    Tuo S, Yong L, Li Y, Lin Y, Lu Q (2017) HSTLBO: a hybrid algorithm based on harmony search and teaching-learning-based optimization for complex high-dimensional optimization problems. PLoS ONE 12(4):e0175114CrossRefGoogle Scholar
  47. 47.
    Mirjalili S, Wang GG, Coelho LDS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435CrossRefGoogle Scholar
  48. 48.
    Yang XS (2017) Social algorithms. In: Meyers RA (ed) Encyclopedia of complexity and systems sci-enc. Springer, BerlinGoogle Scholar
  49. 49.
    Ge S, Feng L, Liu H (2011) The planning of electric vehicle charging station based on grid partition method. In: International conference on electrical and control engineering (ICECE), 2011, pp 2726–2730. IEEEGoogle Scholar
  50. 50.
    Pazouki S, Mohsenzadeh A, Haghifam MR (2013) Optimal planning of PEVs charging stations and demand response programs considering distribution and traffic networks. In: Smart Grid Conference (SGC), 2013, pp 90–95. IEEEGoogle Scholar
  51. 51.
    Mohsenzadeh A, Pang C, Pazouki S, Haghifam M (2015) Optimal siting and sizing of electric vehicle public charging stations considering smart distribution network reliability. In: North American Power Symposium (NAPS), 2015, pp 1–6. IEEEGoogle Scholar
  52. 52.
    Pazouki S, Mohsenzadeh A, Haghifam MR, Ardalan S (2015) Simultaneous allocation of charging stations and capacitors in distribution networks improving voltage and power loss. Can J Electr Comput Eng 38(2):100–105CrossRefGoogle Scholar
  53. 53.
    Mohsenzadeh A, Pazouki S, Ardalan S, Haghifam MR (2018) Optimal placing and sizing of parking lots including different levels of charging stations in electric distribution networks. Int J Ambient Energy 39(7):743–750CrossRefGoogle Scholar
  54. 54.
    Shojaabadi S, Abapour S, Abapour M, Nahavandi A (2016) Optimal planning of plug-in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties. IET Gener Transm Distrib 10(13):3330–3340CrossRefGoogle Scholar
  55. 55.
    Wang S, Xu Y, Dong ZY, Zhao J, Yao W, Luo F, Wang Y (2016) A stochastic collaborative planning approach for electric vehicle charging stations and power distribution system. In: Power and Energy Society General Meeting (PESGM), 2016, pp 1–5. IEEEGoogle Scholar
  56. 56.
    Islam MM, Mohamed A, Shareef H (2015) Optimal allocation of rapid charging stations for electric vehicles. In: IEEE student conference on research and development (SCOReD), 2015, pp 378–383. IEEEGoogle Scholar
  57. 57.
    Deb S, Gao XZ, Tammi K, Kalita K, Mahanta A (xxxx) Pareto dominance based multi-objective chicken swarm optimization and teaching learning based optimization algorithm for charging station placement problem. Swarm and Evolutionary Computation, Elsevier (under review)Google Scholar
  58. 58.
    Phonrattanasak P, Leeprechanon N (2014) Optimal placement of EV fast charging stations considering the impact on electrical distribution and traffic condition. In: International conference on and utility exhibition on green energy for sustainable development (ICVE), Pattaya, pp 1–6Google Scholar
  59. 59.
    Liu ZF, Zhang W, Ji X, Li K (2012) Optimal planning of charging station for electric vehicle based on particle swarm optimization. In: Innovative smart grid technologies-Asia (ISGT Asia), 2012 IEEE, pp 1–5Google Scholar
  60. 60.
    Lin W, Hua G (2015) The flow capturing location model and algorithm of electric vehicle charging stations. In: International conference on logistics, informatics and service sciences (LISS), 2015, pp 1–6. IEEEGoogle Scholar
  61. 61.
    Chen YW, Cheng CY, Li SF, Yu CH (2018) Location optimization for multiple types of charging stations for electric scooters. Appl Soft Comput 67:519–528CrossRefGoogle Scholar
  62. 62.
    Islam MM, Shareef H, Mohamed A (2018) Optimal location and sizing of fast charging stations for electric vehicles by incorporating traffic and power networks. IET Intel Transport Syst 12(8):947–957CrossRefGoogle Scholar
  63. 63.
    Islam MM, Shareef H, Mohamed A (2017) Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm. PLoS ONE 12(12):e0189170CrossRefGoogle Scholar
  64. 64.
    Aljanad A, Mohamed A, Shareef H, Khatib T (2018) A novel method for optimal placement of vehicle-to-grid charging stations in distribution power system using a quantum binary lightning search algorithm. Sustain Cities Soc 38:174–183CrossRefGoogle Scholar
  65. 65.
    Islam M, Shareef H, Mohamed A (2016) Optimal siting and sizing of rapid charging station for electric vehicles considering Bangi city road network in Malaysia. Turk J Electr Eng Comput Sci 24:5Google Scholar
  66. 66.
    Awasthi A, Venkitusamy K, Padmanaban S, Selvamuthukumaran R, Blaabjerg F, Singh AK (2017) Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. Energy 133:70–78CrossRefGoogle Scholar
  67. 67.
    Ho YC, Pepyne DL (2002) Simple explanation of the no-free-lunch theorem and its implications. Optim Theory Appl 115(3):549–570MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© CIMNE, Barcelona, Spain 2019

Authors and Affiliations

  • Sanchari Deb
    • 1
    Email author
  • Xiao-Zhi Gao
    • 2
  • Kari Tammi
    • 3
  • Karuna Kalita
    • 4
  • Pinakeswar Mahanta
    • 4
    • 5
  1. 1.Centre of EnergyIndian Institute of TechnologyGuwahatiIndia
  2. 2.School of ComputingUniversity of Eastern FinlandKuopioFinland
  3. 3.Department of Mechanical EngineeringAalto UniversityEspooFinland
  4. 4.Department of Mechanical EngineeringIndian Institute of TechnologyGuwahatiIndia
  5. 5.Department of Mechanical EngineeringNational Institute of TechnologyYupiaIndia

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