Hypervolume-Driven Analytical Programming for Solar-Powered Irrigation System Optimization

  • T. Ganesan
  • I. Elamvazuthi
  • Ku Zilati Ku Shaari
  • P. Vasant
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 210)


In the field of alternative energy and sustainability, optimization type problems are regularly encountered. In this paper, the Hypervolume-driven Analytical Programming (Hyp-AP) approaches were developed. This method was then applied to the multiobjective (MO) design optimization of a real-world photovoltaic (PV)-based solar powered irrigation system. This problem was multivariate, nonlinear and multiobjective. The Hyp-AP method was used to construct the approximate Pareto frontier as well as to identify the best solution option. Some comparative analysis was performed on the proposed method and the approach used in previous work.


Solar power photovoltaic (PV) irrigation system multiobjective (MO) Optimization analytical programming (AP) hypervolume indicator (HVI) 


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  1. Elamvazuthi, I., Ganesan, T., Vasant, P.: A Comparative Study of HNN and Hybrid HNN-PSO Techniques in the Optimization of Distributed Generation (DG) Power Systems. In: International Conference on Advance Computer Science and Information System (ICACSI 2011), pp. 195–199 (2011)Google Scholar
  2. Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.: Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production. J. of Appl. Energy 103, 368–374 (2013)CrossRefGoogle Scholar
  3. Helikson, H.J., Haman, D.Z., Baird, C.D.: Pumping water for irrigation using solar enegy, Fact Sheet (EES-63). University of FloridaGoogle Scholar
  4. Wong, Y.W., Sumathy, K.: Thermodynamic analysis and optimization of a solar thermal water pump. Appl. Thermal Eng. 21, 613–627 (2001)CrossRefGoogle Scholar
  5. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  7. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181(3), 1653–1669 (2007)MATHCrossRefGoogle Scholar
  8. Zelinka, I.: Analytic programming by Means of Soma Algorithm. In: Proceeding of the 8th International Conference on Soft Computing, Mendel 2002, Brno, Czech Republic, pp. 93–101 (2002)Google Scholar
  9. Zelinka, I., Oplatkova, Z.: Analytic programming – Comparative Study. In: Second International Conference on Computational Intelligence, Robotics, and Autonomous Systems CIRAS 2003, Singapore (2003)Google Scholar
  10. Chen, W., Kwok-Leung, T., Allen, J.K., Mistree, F.: Integration of the Response Surface Methodology with the compromise decision support problem in developing a general robust design procedure. In: Azarm, S., et al. (eds.) Advances in Design Automation, vol. 82-2, ASME, New York (1995)Google Scholar
  11. Koza, J.R.: Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, USA (1992)MATHGoogle Scholar
  12. Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: Evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, p. 83. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. Koza, J.R.: Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, USA (1992)MATHGoogle Scholar
  14. Mistree, F., Hughes, O.F., Bras, B.A.: The Compromise Decision Support Problem and the Adaptive Linear Programming Algorithm. In: Structural Optimization: Status and Promise, pp. 247–286. AIAA, Washington, D.C (1993)Google Scholar
  15. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology. In: Control and Artificial Intelligence. MIT Press, USA (1992)Google Scholar

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© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • T. Ganesan
    • 1
  • I. Elamvazuthi
    • 2
  • Ku Zilati Ku Shaari
    • 1
  • P. Vasant
    • 3
  1. 1.Department of Chemical EngineeringUniversity Technology PetronasTronohMalaysia
  2. 2.Department of Electrical & Electronic EngineeringUniversity Technology PetronasTronohMalaysia
  3. 3.Department of Fundamental & Applied SciencesUniversity Technology PetronasTronohMalaysia

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