The Solving of Linear Programmable Problems Using Hybrid Algorithms

  • Authors

    • Yasameen M. Mohammed
    • . .
    • . .
    • . .
  • Hybrid Algorithms, Linear Programming, Absolute Value
  • This paper presents the pointing and investigation of an effectiveness relating a linear mathematical formulation to solve an linear programmable problems based on hybrid algorithms. The hybrid is packages of mathematical programs which contain much occupation required for solve many criteria of linear program (LP) problems. Additionally, a single criterion with linear quadratic problems is solved in this work. For dynamic problems, the hybrid algorithms are useful because the practical algorithm develop the configuration of dynamic problems. The advantage of handling dynamic problems to the user is to produce a simple method of criterion formulation model. The orientation of hybrid is interactive form of process whose a series of problems are answered based changeable situation such as dissimilar objectives functions. The multi objectives criteria could be simply defined and efficient to assist the package. Additionally, the hybrid present more options to diagnostic and verify the problem solving. The suggested approach is hybrid algorithms to solve absolute value equation with no supposition and solvability in linear systems equations followed by linear programs iteratively. The proposed algorithms has been tested and investigated properly.


  • References

    1. [1] Propoi, A. Problems of Dynamic Linear Programming, nASA, RM-76-78. Sosnowski, J.S. (1978). Dynamic optimization of multisectorial linear production model. Systems Research Institute, Warsaw, Ph.D. Thesis, 1976

      [2] P. P. Bedekar and S. R. Bhide, “Optimum coordination of directional overcurrent relays using the hybrid GA-NLP approach,†IEEE Trans. Power Deliv., vol. 26, no. 1, pp. 109–119, 2011.

      [3] Shakeel PM, Manogaran G., “Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural networkâ€, Health and Technology, 2018:1-9.

      [4] l. darrell whitley, " a hybrid genetic algorithm for the quadratic assignment problem", proceeding gecco,00 proceeding of the 2nd annual conference on genetic and evoluation computation. page 135-142. las vegas, nevada, july 2000

      [5] M. H. Ali, M. F. Zolkipli, M. A. Mohammed, and M. M. Jaber, “Enhance of extreme learning machine-genetic algorithm hybrid based on intrusion detection system,†J. Eng. Appl. Sci., vol. 12, no. 16, 2017.

      [6] S. Ralhan and S. Ray, “Directional overcurrent relays coordination using linear programming intervals: A comparative analysis,†2013 Annu. IEEE India Conf. INDICON 2013

      [7] Mohand Ouamer Bibi and Mohand Bentobache. A hybrid direction algorithm for solving linear programs. International Journal of Computer Mathematics, 92(2) :200_216, 2015.

      [8] Mohand Bentobache and Mohand Ouamer Bibi. Numerical Methods of Linear and Quadratic Programming. French Academic Presses, Germany, 2016 (in french).

      [9] M. A. Mohammed et al., “Genetic case-based reasoning for improved mobile phone faults diagnosis,†Comput. Electr. Eng., 2018.

      [10] Lootsma, F.A., "Optimization with Multiple Objectives. In Iri M and Tanabe K (eds.) Mathematical Programming, Recent Developments and Applications. KTK Scientific, Publishers: Tokyo., 1989

      [11] P. M. Shakeel, S. Baskar, V. R. S. Dhulipala, S. Mishra, and M. M. Jaber, “Maintaining security and privacy in health care system using learning based Deep-Q-Networks,†J. Med. Syst., vol. 42, no. 10, p. 186, 2018.

      [12] Steuer, R., " Multiple criteria optimization: Theory, Computation and Application". Chichester, John Wiley & Sons: New York, 1986

      [13] Kok, M. and Lootsma, F.A., "Pairwise comparison methods in multiple objective programming with applications in along-term energy planning model". Eur J Opl Res 22: 44, 1985

      [14] Lai, T.Y., "IMOST: Interactive Multiple Objective System Technique". J Opl Res Soc 46: 958-976, 1995

      [15] Preeth, S.K.S.L., Dhanalakshmi, R., Kumar, R.,Shakeel PM.An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system.Journal of Ambient Intelligence and Humanized Computing.2018:1–13.

      [16] Daniela Pucci de Farias and Benjamin Van Roy. On constraint sampling for the linear programming approach to approximate dynamic programming. Mathematics of Operations Research, 29(3):462–478, 2004.

      [17] Shakeel, P.M., Tolba, A., Al-Makhadmeh, Zafer Al-Makhadmeh, Mustafa Musa Jaber, “Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networksâ€, Neural Computing and Applications,2019,pp1-14.

      [18] C. BASNET, "A hybrid genetic algorithm for a loading problem in flexible manufacturing systems", Department of Management Systems The University of Waikato, Hamilton, New Zealand,at :

      [19] Manogaran G, Shakeel PM, Hassanein AS, Priyan MK, Gokulnath C. Machine-Learning Approach Based Gamma Distribution for Brain Abnormalities Detection and Data Sample Imbalance Analysis. IEEE Access. 2018 Nov 9.DOI 10.1109/ACCESS.2018.2878276

      [20] Daniela Pucci de Farias and Benjamin Van Roy, "The linear programming approach to approximate dynamic programming", Operations Research, 51(6):850–856, 2003.

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  • How to Cite

    M. Mohammed, Y., ., ., ., ., & ., . (2018). The Solving of Linear Programmable Problems Using Hybrid Algorithms. International Journal of Engineering & Technology, 7(3.20), 832-835.