6th International Young Scientist Congress (IYSC-2020) will be Postponed to 8th and 9th May 2021 Due to COVID-19. 10th International Science Congress (ISC-2020).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Qualitative Analysis of Stochastic Operations in Dual Axis Solar Tracking Environment

Author Affiliations

  • 1Department of Electronic and Communication Engineering, University, Tenaga Nasional, Jalan Ikram-Uniten, Kajang, Selangor, MALAYSIA

Res. J. Recent Sci., Volume 1, Issue (9), Pages 74-78, September,2 (2012)


This research reviews the major contributions to the solar tracking field from a normal mechanical turning single axis to double axis which continuously evolve to the application of different evolutional algorithm’s methods in optimizing solar tracking system. This literature review shows that heuristic methods have outperformed other classical approaches in maximizing the performance of solar tracking system. Detailed discussion on solar tracker together with the evolution of artificial intelligent methods such as genetic algorithm simulated annealing and threshold acceptance is materialised in this paper. In this research, genetic algorithm has been identified with its superiority in searching for optimal solution due to its robustness. Both software and hardware have been developed to simulate related genetic algorithm results compared to different optimization search. Simulation results demonstrated the ability of GA to converge to best fitness value at 0.98021 with the axles X and Y pointing to +3 degree and +2 degree respectively in relation to sun’s position compared to simulated annealing and threshold acceptance.


  1. Spall J.C, Introduction to Stochastic Search and Optimization Wiley, Available online at http://www.jhuapl.edu/ISSO (2003)
  2. Kirkpatrick S., Jr C.D.G. and Vecchi M.P., Optimization by Simulated Annealing, Science,220, 671-680 (1983)
  3. Kirkpatrick S., Optimization by simulated annealing: quantitative studies, Statistical Physics, 34, 975-986 (1984)
  4. Cerny V., A thermo dynamical approach to the Traveling salesman problem:An efficient simulation algorithm, Optimization Theory and Applications, 45, 41-51 (1985)
  5. Glover F., Future paths for integer programming and links to artificial Intelligence, Computers and Operations Research, 13, 533-549 (1986)
  6. Glover F., Tabu search: Part I, ORSA Journal on Computing, ,190-20 (1989)
  7. Glover F., Tabu search: Part II, ORSA Journal onComputing, , 4-32 (1990)
  8. Kohonen T., Self-organization and associative memory, Springer (1984)
  9. Rumelhart D.E. and McClelland J.L., Parallel distribute processing: Explorations in the microstructures of cognition, , foundations. Cambridge, Mass, MIT Press, 1stedition (1986)
  10. McClelland J.L., Rumelhart D.E., Parallel distributed processing: explorations in the microstructures of cognition, Vol : psychological and biological models, Cambridge, Mass, MIT Press, 1st edition (1986)
  11. Holland J., Adaptation in Natural and Artificial Systems, The University of Michigan Press (1975)
  12. Goldberg D.E., Genetic Algorithm in Search, Optimization and Machine Learning, Addison-Wesley Pub.Co., Inc (1989)
  13. Davis L., Handbook of Genetic Algorithm, Von Nostrand Reinhold (1991)
  14. Khan M.F. and Ali R.L., Automatic sun tracking system, presented at the All Pakistan Engineering Conference, Islamabad, Pakistan (2005)
  15. Khlaichom P. and Sonthipermpoon K. Optimization of solar tracking system based on genetic algorithms, http://www.thaiscience.info / (2006)
  16. Syamsiah Mashohor, Evaluation of Genetic Algorithm based Solar Tracking System for Photovoltaic Panels; ICSET(2008)
  17. Andre J., Siarry P. and Dognon T., An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization, Advances in engineering software, 32(1), 49–60 (2001)
  18. Smith J.E. and Fogarty T.C., Operator and parameter adaptation in genetic algorithms, Soft computing, a fusion of foundations, Methodologies and Applications, 92(2), 81–87 (1997)
  19. Jung S.H., Queen-bee evolution for genetic algorithms, Electronics Letters, 39, 575–576 (2003)
  20. Fam D.F., Koh S.P., Tiong S.K. and Chong K.H., Analysis of Convergence Effect Via Different Genetic Operations, Conference of ASEAN Federation of Engineering Organization, Hanoi, Vietnam (2010)
  21. Nikhil D., Rajesh A., Vijay M., Sandeep K., Mohit, Satyapal and Pardeep K., Thermodynamic Analysis of a Combined Heat and Power System, Res. J. Recent Sci.,1(3), 76-79 (2012)
  22. Sharifi M. and Shahriari B, Pareto Optimization of Vehicle Suspension Vibration for a Nonlinear Half Car Model Using a Multi-Objective Genetic Algorithm, Res. J. Recent Sci.,1(8), 17-22 (2012)
  23. Mitsuo G. and Runwei C., Genetic Algorithm and Engineering Design, New York, Wiley (1997)