Skip to main content

Advertisement

SpringerLink
Log in

Integration of fuzzy logic with Metaheuristics for education center site selection

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Education is one of the most vital sectors of any nation’s development. Site selection for Education Centers (EC) like schools, colleges, and coaching centers can be a very complex process. Various parameters like population, literacy rate, property cost, etc. have to be considered while selecting a site. Though deterministic approaches employed for site selection have been proven to give the best possible solution, they fail to work on large datasets. Recently metaheuristics have become very popular for solving optimization problems. This paper presents two integrated approaches, Fuzzy Genetic Algorithm for EC site selection (FGA-ECSS) and Fuzzy Binary Particle Swarm Optimization for EC site selection (FBPSO-ECSS) for choosing sites optimally. To evaluate the effectiveness of the two approaches, FGA-ECSS and FBPSO-ECSS have been compared with each other as well as with Genetic Algorithm and Binary Particle Swarm Optimization. The results obtained from the proposed solutions are promising and indicate that they can be used for solving such optimization problems.

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

Access this article

Log in via an institution

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Alhaffa, A., Abdulal, W. (2011). A market-based study of optimal ATM'S deployment strategy. International Journal of Machine Learning and Computing, 104-112.

  • Ali, K. A. (2018). Multi-criteria decision analysis for primary school site selection in Al-Mahaweel district using GIS technique. Journal of Kerbala University, 14(1), 342–350.

    Google Scholar 

  • Arı, E. S., & Gencer, C. (2020). The use and comparison of a deterministic, a stochastic, and a hybrid multiple-criteria decision-making method for site selection of wind power plants: An application in Turkey. Wind Engineering, 44(1), 60–74.

    Article  Google Scholar 

  • Charles, Robin, Fleming, Peter John. (2002). Why use elitism and sharing in a multi-objective genetic algorithm? Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, 520–527.

  • Chehreghan, A., Rajabi, M., Pazoki, S.H. (2013). Developing a novel method for optimum site selection based on fuzzy genetic system and GIS.

  • Darani, S. K., Eslami, A., Jabbari, M., & Asefi, H. (2018). Parking lot site selection using a fuzzy AHP-TOPSIS framework in Tuyserkan, Iran. Journal of Urban Planning and Development, 144(3).

  • Erdin, C., & Ozkaya, G. (2019). Turkey’s 2023 energy strategies and investment opportunities for renewable energy sources: Site selection based on ELECTRE. Sustainability, 11, 2136.

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. MIT press.

  • Hosseini, S.M., Fuente, A.D., & Pons, O. (2016). Multicriteria decision-making method for sustainable site location of post-disaster temporary housing in urban areas,.

  • Hwang CL., Yoon K. (1981) Methods for multiple attribute decision making. In: Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems, 186.

  • Kapilan, S., & Elangovan, K. (2018). Potential landfill site selection for solid waste disposal using GIS and multi-criteria decision analysis (MCDA). Journal of Central South University, 25(3), 570–585.

    Article  Google Scholar 

  • Kennedy, J. P., & Eberhart, R. (1997). A discrete binary version of the particle swarm algorithm. 1997 IEEE international conference on systems, man, and cybernetics. Computational Cybernetics and Simulation, 5, 4104–4108.

    Google Scholar 

  • Koseoglu, B., Buber, M., & Toz, A. C. (2018). Optimum site selection for oil spill response center in the Marmara Sea using the AHP-TOPSIS method. Archives of Environmental Protection, 44(4), 38–49.

    Google Scholar 

  • Kumar, S., & Chaturvedi, D. K. (2013). Optimal power flow solution using fuzzy evolutionary and swarm optimization. International Journal of Electrical Power & Energy Systems, 47, 416–423.

    Article  Google Scholar 

  • Lawler, E., & Bell, M. (1966). A method for solving discrete optimization problems. Operations Research, 14(6), 1098–1112.

    Article  Google Scholar 

  • Lee, J. (2018). Understanding site selection of for-profit educational management organization charter schools. Education Policy Analysis Archives, 26, 77.

    Article  Google Scholar 

  • Liu, J., Li, P., Shi, T., & Ma, X. (2016). Optimal site selection of China railway data centers by the PSO algorithm. 2016 12th World Congress on Intelligent Control and Automation (WCICA), 251-257.

  • Liu, J., Xiao, Y., Wang, D., & Pang, Y. (2018). Optimization of site selection for construction and demolition waste recycling plant using genetic algorithm. Neural Computing and Applications, 31, 233–245.

    Article  Google Scholar 

  • Makaan. (2007). Property Rates in India - 2019. Retrieved from https://www.makaan.com/price-trends

  • Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149(B), 153–165.

    Article  Google Scholar 

  • Moussa, M., Mostafa, Y., & Elwafa, A. (2017). School site selection process. Procedia Environmental Sciences, 37, 282–293.

    Article  Google Scholar 

  • NRI Online Pvt. Ltd. (1997). India's 100 Biggest Cities, Largest Cities in India. Retrieved from https://www.nriol.com/india-statistics/biggest-cities-india.asp

  • Saaty, T. L. (1988). What is the analytic hierarchy process. Mathematical Models for Decision Support, 48, 109–121.

    Article  MathSciNet  Google Scholar 

  • Senvar, O., Otay, İ., & Boltürk, E. (2016). Hospital site selection via hesitant fuzzy TOPSIS. IFAC-Papers On Line, 49, 1140–1145.

    Article  Google Scholar 

  • Shi, Y., & Eberhart, R. C. (2001). Fuzzy adaptive particle swarm optimization. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 1, 101–106.

    Article  Google Scholar 

  • Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Systems, Man, and Cybernetics, 24, 656–667.

    Article  Google Scholar 

  • Tian, D., & Li, N. (2009). Fuzzy particle swarm optimization algorithm. International Joint Conference on Artificial Intelligence, 2009, 263–267.

  • Umbarkar, A. K., & Sheth, P. (2015). Crossover operators in genetic algorithms: A REVIEW. ICTACT Journal on Soft Computing, 6(1).

  • Varnamkhasti, M. J., & Lee, L. S. (2012). A fuzzy genetic algorithm based on binary encoding for solving multidimensional knapsack problems. Journal of Applied Mathematics.

  • Wu, Y., Zhang, J., Yuan, J., Geng, S., & Zhang, H. (2016). Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China. Energy Conversion and Management, 113(1), 66–81.

    Article  Google Scholar 

  • Yang, Xin-She. (2011). Review of meta-heuristics and generalised evolutionary walk algorithm. International Journal Bio-Inspired Comput., 77–84.

  • Yeniay, Ö. (2005). Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications, 10(1), 45–56.

    Article  MathSciNet  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets*. Information and Control, 8(3), 338–353.

    Article  MathSciNet  Google Scholar 

  • Zhu, H. (2016). Logistics distribution Centre site selection based on domain mean value optimization PSO algorithm.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India

    Aastha Agrawal & Anjali Agarwal

  2. Department of Information Technology, Netaji Subhas University of Technology, Dwarka, New Delhi, India

    Priti Bansal

Authors
  1. Aastha Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anjali Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Priti Bansal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anjali Agarwal.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agrawal, A., Agarwal, A. & Bansal, P. Integration of fuzzy logic with Metaheuristics for education center site selection. Educ Inf Technol 26, 103–124 (2021). https://doi.org/10.1007/s10639-020-10254-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-020-10254-9

Keywords

Search

Navigation