Improving the Performance of the Optimization Technique Using Chaotic Algorithm

  • R. Arunkumar
  • V. Jothiprakash
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Optimizing the operations of a multi-reservoir systems are complex because of their larger dimension and convexity of the problem. The advancement of soft computing techniques not only overcomes the drawbacks of conventional techniques but also solves the complex problems in a simple manner. However, if the problem is too complex with hardbound variables, the simple evolutionary algorithm results in slower convergence and sub-optimal solutions. In evolutionary algorithms, the search for global optimum starts from the randomly generated initial population. Thus, initializing the algorithm with a better initial population not only results in faster convergence but also results in global optimal solution. Hence in the present study, chaotic algorithm is used to generate the initial population and coupled with genetic algorithm (GA) to optimize the hydropower production from a multi-reservoir system in India. On comparing the results with simple GA, it is found that the chaotic genetic algorithm (CGA) has produced slightly more hydropower than simple GA in fewer generations and also converged quickly.


Optimization Genetic algorithm Chaotic algorithm  Multi-hydropower system. 



The authors gratefully acknowledge the Ministry of Water Resources, Government of India, New Delhi, for sponsoring this research project. The authors also thank Chief Engineer, KHEP, Executive Engineer, Koyna Dam and Executive Engineer, Kolkewadi Dam for providing the necessary data.


  1. 1.
    Wardlaw, R., Sharif, M.: Evaluation of genetic algorithms for optimal reservoir system operation. J. Water Resour. Plann. Manage. 125(1), 25–33 (1999)Google Scholar
  2. 2.
    Sharif, M., Wardlaw, R.: Multireservoir systems optimization using genetic algorithms: case study. J. Comput. Civ. Eng. 14(4), 255–263 (2000)CrossRefGoogle Scholar
  3. 3.
    Jothiprakash, V., Shanthi, G., Arunkumar, R.: Development of operational policy for a multi-reservoir system in India using genetic algorithm. Water Resour. Manage. 25(10), 2405–2423 (2011)CrossRefGoogle Scholar
  4. 4.
    Yuan, X., Yuan, Y., Zhang, Y.: A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Math. Comput. Simul. 59(4), 319–327 (2002)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Cheng, C.T., Wang, W.C., Xu, D.M., Chau, K.: Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour. Manage. 22(7), 895–909 (2008)CrossRefGoogle Scholar
  6. 6.
    Han, F., Lu, Q.S.: An improved chaos optimization algorithm and its application in the economic load dispatch problem. Int. J. Compt. Math. 85(6), 969–982 (2008)Google Scholar
  7. 7.
    May, R.M.: Simple mathematical models with very complicated dynamics. Nature 261(5560), 459–467 (1976)CrossRefGoogle Scholar
  8. 8.
    Huang, X., Fang, G., Gao, Y., Dong, Q.: Chaotic optimal operation of hydropower station with ecology consideration. Energy Power Eng. 2(3), 182–189 (2010)CrossRefGoogle Scholar
  9. 9.
    KHEP: Koyna hydro electric project stage-IV. Irrigation Department, Government of Maharashtra, (2005)Google Scholar
  10. 10.
    Jothiprakash, V., Arunkumar, R.: Optimization of hydropower reservoir using evolutionary algorithms coupled with chaos. Water Resour. Manage. (2013). doi:10.1007/s11269-013-0265-8, (Published online)Google Scholar
  11. 11.
    KWDT: Krishna water disputes tribunal: The report of the Krishna water disputes tribunal with the decision. Ministry of water resources, Government of India. New Delhi (2010)Google Scholar
  12. 12.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithm. Wiley, New Jersey (2001)Google Scholar
  13. 13.
    Williams, G.P.: Chaos Theory Tamed. Joseph Henry Press, Washington, D.C (1997)MATHGoogle Scholar
  14. 14.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)MATHMathSciNetGoogle Scholar
  15. 15.
    Loucks, D.P., Stedinger, J.R., Haith, D.A.: Water Resources Systems Planning and Analysis. Prentice Hall Inc, Englewood Cliffs, New Jersey (1981)Google Scholar
  16. 16.
    Arunkumar, R., Jothiprakash, V.: Optimal reservoir operation for hydropower generation using non-linear programming model. J. Inst. Eng. (India) 93(2), 111–120 (2012)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology BombayMumbaiIndia

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