Chaotic Immune Symbiotic Organisms Search Algorithm for Solving Optimisation Problem

  • Authors

    • Mohamad Khairuzzaman Mohamad Zamani
    • Ismail Musirin
    • Saiful Izwan Suliman
    • Sharifah Azma Syed Mustaffa
    https://doi.org/10.14419/ijet.v7i3.15.17505

    Received date: August 14, 2018

    Accepted date: August 14, 2018

    Published date: August 13, 2018

  • Benchmark Test Functions, Chaotic Immune Symbiotic Organisms Search, Chaotic Local Search

  • Abstract

    Achieving an optimal solution is very crucial while solving a problem. To achieve the optimality required, optimisation techniques can be implemented while solving the problem. The presence of classical optimisation techniques has enabled an optimal solution to be obtained. However, as the complexity of the optimisation problem increased, classical optimisation techniques faced difficulties in providing optimal solutions. Heuristics-based algorithms were introduced to counter the problem faced by classical optimisation techniques. Good performance of these heuristics-based algorithm has been implied through various implementation in solving optimisation problems. Despite the performance of these algorithms, the flaws of these algorithms hinder them from producing high-quality results. To mitigate the problem, this paper presents the development of Chaotic Immune Symbiotic Organisms Search algorithm which was inspired by the element of diversification as well as the increased capability of exploration. The performance of the proposed algorithm has been tested by solving several benchmark test functions. A comparative study was also conducted with respect to several other existing optimisation algorithms resulted in the superiority of the proposed algorithm in providing high-quality solutions.

  • References

    1. J. Li, D. Wang, W. Wang & J. Jiang, “Minimize Current Stress of Dual-Active-Bridge DC-DC Converters for Electric Vehicles Based on Lagrange Multipliers Method”, Energy Procedia, Vol. 105, (2017), pp. 2733 – 2738.
    2. V. Torczon, R.M. Lewis & M.W. Trosset, “Direct search methods: then and now”, Journal of Computational and Applied Mathematics, Vol. 124, No. 1-2, (2000), pp. 191 – 207.
    3. C. Hamzaçebi & F. Kutay, “Continuous functions minimization by dynamic random search technique”, Applied Mathematical Model-ling, Vol. 31, No. 10, (2007), pp. 2189 – 2198.
    4. J.R. Ribeiro Cavalcante & F.M. Campello de Souza, “A Global Op-timization Algorithm for Solving Linear Programming Problem”, IFAC Proceedings Volumes, Vol. 30, No. 19, (1997), pp. 513 – 515.
    5. L.L. Lai & J.T. Ma, “Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach”, IEEE Transactions on Power Systems, Vol. 12, No. 1, (1997), pp. 198 – 206.
    6. K.Y. Lee & F.F. Yang, “Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary pro-gramming, evolutionary strategy, genetic algorithm, and linear pro-gramming”, IEEE Transactions on Power Systems, Vol. 13, No. 1, (1998), pp. 101 – 108.
    7. C.C. Asir Rajan, M. Surya Kalavathi & S. Ranganathan, “Self-adaptive firefly algorithm based multi-objectives for multi-type FACTS placement”, IET Generation, Transmission & Distribution, Vol. 10, No. 11, (2016), pp. 2576 – 2584.
    8. T. Sen & H.D. Mathur, “A new approach to solve Economic Dis-patch problem using a Hybrid ACO–ABC–HS optimization algo-rithm”, International Journal of Electrical Power & Energy Systems, Vol. 78, (2016), pp. 735 – 744.
    9. M.T. Bhoskar, M.O.K. Kulkarni, M.N.K. Kulkarni, M.S.L. Patekar, G.M. Kakandikar & V.M. Nandedkar, “Genetic Algorithm and its Applications to Mechanical Engineering: A Review”, Materials To-day: Proceedings, Vol. 2, No. 4-5, (2015), pp. 2624 – 2630.
    10. C.M. Chan, H.L. Bai & D.Q. He, “Blade shape optimization of the Savonius wind turbine using a genetic algorithm”, Applied Energy, Vol. 213, (2018), pp. 148 – 157.
    11. S. Parinam, A.L. Sharma, V.S.R.S. Praveen Kumar, M. Kumar, N. Kumari, S.K. Mittal & V. Karar, “Optimization of optical parame-ters for the design of multilayer bandpass filter using genetic algo-rithm”, Materials Today: Proceedings, Vol. 5, No. 2, (2018), pp. 5091 – 5096.
    12. K.F. Fong, V.I. Hanby & T.T. Chow, “HVAC system optimization for energy management by evolutionary programming”, Energy and Buildings, Vol. 38, No. 3, (2006), pp. 220 – 231.
    13. B.N.S. Rahimullah, E.I. Ramlan & T.K. Abdul Rahman, “Evolu-tionary approach for solving economic dispatch in power system”, Proceedings. National Power Engineering Conference, 2003. PECon 2003, (2003), pp: 32 – 36.
    14. S. Domínguez-Isidro, E. Mezura-Montes & L.G. Osorio-Hernández, “Evolutionary programming for the length minimization of addition chains”, Engineering Applications of Artificial Intelligence, Vol. 37, (2015), pp. 125 – 134.
    15. C.L. Liao, S.J. Lee, Y.S. Chiou, C.R. Lee, C.H. Lee, “Power con-sumption minimization by distributive particle swarm optimization for luminance control and its parallel implementations” Expert Sys-tems with Applications, Vol. 96, (2018), pp. 479 – 491.
    16. A. Mortazavi & V. Toğan, “Sizing and layout design of truss struc-tures under dynamic and static constraints with an integrated parti-cle swarm optimization algorithm”, Applied Soft Computing, Vol. 51, (2017), pp. 239 – 252.
    17. M. Awais, A. Basit, R. Adnan, Z.A. Khan, U. Qasim, T. Shafique & N. Javaid, “Overload management in transmission system using particle swarm optimization”, Procedia Computer Science, Vol. 52, No. 1, (2015), pp. 858 – 865.
    18. T.K. Nizami & K. Sundareshwaran, “A feedback control design of buck converter: An artificial immune system based approach”, 2015 39th National Systems Conference (NSC), (2015), pp: 1 – 6.
    19. K. Bamdad, M.E. Cholette, L. Guan & J. Bell, “Ant colony algo-rithm for building energy optimisation problems and comparison with benchmark algorithms”, Energy and Buildings, Vol. 154, (2017), pp. 404 – 414.
    20. K. Rayudu, G. Yesuratnam & A. Jayalaxmi, “Artificial Bee Colony algorithm for optimal reactive power dispatch to improve voltage stability”, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), (2016), pp: 1 – 7.
    21. T. Apostolopoulos & A. Vlachos, “Application of the Firefly Algo-rithm for Solving the Economic Emissions Load Dispatch Problem”, International Journal of Combinatorics, Vol. 2011, (2011), pp. 1 – 23.
    22. S. Yazdani, H. Nezamabadi-Pour & S. Kamyab, “A gravitational search algorithm for multimodal optimization”, Swarm and Evolu-tionary Computation, Vol. 14, (2014), pp. 1 – 14.
    23. O. Ivanov O & M. Gavrilas, “A hybrid GA-PSO algorithm for stat-ic VAR compensation”, 2016 International Conference and Exposi-tion on Electrical and Power Engineering (EPE), (2016), pp: 681 – 686.
    24. B. Mahdad, K. Srairi & T. Bouktir, “Optimal power flow for large-scale power system with shunt FACTS using efficient parallel GA”, International Journal of Electrical Power & Energy Systems, Vol. 32, No. 5, (2010), pp. 507 – 517.
    25. A. Soeprijanto & M. Abdillah, “Type 2 fuzzy adaptive binary parti-cle swarm optimization for optimal placement and sizing of distrib-uted generation”, 2011 2nd International Conference on Instrumen-tation, Communications, Information Technology, and Biomedical Engineering, (2011), pp: 233 – 238.
    26. R. Sirjani & A. Mohamed, “Improved Harmony Search Algorithm for Optimal Placement and Sizing of Static Var Compensators in Power Systems”, 2011 First International Conference on Informat-ics and Computational Intelligence, (2011), pp: 295 – 300.
    27. M. Cheng & D. Prayogo, “Symbiotic Organisms Search: A new me-taheuristic optimization algorithm”, Computers & Structures, Vol. 139, (2014), pp. 98 – 112.
    28. M.K. Mohamad Zamani, I. Musirin & S.I. Suliman, “Symbiotic Or-ganisms Search Technique for SVC Installation in Voltage Control”, Indonesian Journal of Electrical Engineering and Computer Science, Vol. 6, No. 2, (2017), pp. 318 – 329.
    29. D. Prasad & V. Mukherjee, “A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices”, Engineering Science and Technology, an International Journal, Vol. 19, No. 1, (2016), pp. 79 – 89.
    30. U. Guvenc, S. Duman, M.K. Dosoglu, H.T. Kahraman, Y. Sonmez & C. Yilmaz, “Application of Symbiotic Organisms Search Algo-rithm to solve various economic load dispatch problems”, 2016 In-ternational Symposium on INnovations in Intelligent SysTems and Applications (INISTA), (2016), pp: 1 – 7.
    31. S. Saha & V. Mukherjee, “Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm”, IET Generation, Transmission & Distribution, Vol. 10, No. 14, (2016), pp. 3671 – 3680.

  • Downloads

  • How to Cite

    Khairuzzaman Mohamad Zamani, M., Musirin, I., Izwan Suliman, S., & Azma Syed Mustaffa, S. (2018). Chaotic Immune Symbiotic Organisms Search Algorithm for Solving Optimisation Problem. International Journal of Engineering and Technology, 7(3.15), 73-79. https://doi.org/10.14419/ijet.v7i3.15.17505