A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques

  • Authors

    • Mr. Gireesha. B
    • . .
    https://doi.org/10.14419/ijet.v7i4.5.20205

    Received date: September 24, 2018

    Accepted date: September 24, 2018

    Published date: September 22, 2018

  • Optimizations Techniques (OT), Artificial Intelligence, Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FFA), Fitness functions.
  • Abstract

    From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle Swarm Optimization (PSO) is presented and also the most suitable fitness functions and its numerical expressions have discussed.

  • References

    1. https://en.wikipedia.org/wiki/Mathematical_optimization
    2. "The Nature of Mathematical Programming Archived2014-03-05 at the Wayback Machine.," Mathematical Programming Glossary, IN-FORMS Computing Society.
    3. Jung-Min Yang, Jong-Hwan Kim, “Sliding Mode Control for Tra-jectory Tracking of Nonholonomic Wheeled Mobile Robots”, IEEE Transactions on Robotics and Automation, Vol. 15, No. 3, June 1999, 578-587.
    4. Yogeswaran Mohan, S. G. Ponnambalam, “An Extensive Review of Research in Swarm Robotics”, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), 140–145.
    5. ManueleBrambilla, Eliseo Ferrante, Mauro Birattari, Marco Dorigo. Swarm Intell Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, Springer, 2013, 7 (1), 1-41. <10.1007/s11721-012-0075-2>.
    6. M. R. AlRashidi, M. E. El-Hawary, “A Survey of Particle Swarm Optimization Applications in Electric Power Systems”, IEEE Trans-actions on Evolutionary Computation, Sep. 2009, DOI: 10.1109/TEVC.2006.880326.
    7. Kwang Y Lee, Mohamed A El-Sharkawi, “Modern Heuristic Opti-mization Techniques: Theory and Applications to Power Systems”, IEEE Press, Wiley Interscience, A John Wiley & Sons Inc. Publica-tion, 2008, ISBN 978-0471-45711-4.
    8. Ahmed M Hasan, KhairulmizamSamsudin, Abd Rahman Ramli, Raja SyamsulAzmir, and Salam A Ismaeel, “A Review of Naviga-tionSystems (Integrationand Algorithms)”, Australian Journal of Basic and Applied Sciences, 3(2), 943-959, Jan 2009.
    9. Oren La'adan, Amnon Barak, “Inter Process Communication Opti-mization In A Scalable Computing Cluster”, Annual Review of Scalable Computing. September 2000, 121-173.
    10. Minoru Mukuda, Yasuhiro Tsujimura, “A Multiobjective Genetic Algorithm For Solving Reliability Optimization Problem Of A Communication Network System”, Advanced Reliability Modeling II. July 2006, 166-175.
    11. M. Egmont-Petersena, D. de Ridder b, H. Handels, “Image pro-cessing with neural networks—a review”, Pattern Recognition Soci-ety, Elsevier, 2002, 35 (10), 2279-2301, DOI: 10.1016/s0031-3203(01)00178-9.
    12. Mahamed G. Omran, Andries P. Engelbrecht, Ayed Salman, “Im-age Classification Using Particle Swarm Optimization”, Recent Ad-vances in Simulated Evolution and Learning. August 2004, 347-365.
    13. A. Ghosh and A. Gorai, “Hue-Preserving Color Image Enhance-ment Using Particle Swarm Optimization”, Integration of Swarm Intelligence and Artificial Neural Network. June 2011, 157-178.
    14. João Manuel, R. S. Tavares,Luísa Ferreira Bastos, “Improvement of Modal Matching Image Objects in Dynamic Pedobarography Using Optimization Techniques”, Progress in Computer Vision and Image Analysis. August 2009, 339-368.
    15. HUA-MEI CHEN and PRAMOD K. VARSHNEY, “Techniques for Mutual Information-Based Brain Image Registration and Their Applications”, Medical Imaging Systems Technology. August 2005, 325-350.
    16. A.Colorni, M.Dorigo, V.Maniezzo, "Distributed Optimization by Ant Colonies," Proceedings of the First European Conference on Artificial Life, Paris, France, F.Varela and P.Bourgine (Eds.), Else-vier Publishing, 134–142, 1991.
    17. A.Colorni, M.Dorigo, V.Maniezzo, "An Investigation of some Properties of an Ant Algorithm," Proceedings of the Parallel Prob-lem Solving from Nature Conference (PPSN 92), Brussels, Belgium, R.Männer and B.Manderick (Eds.), Elsevier Publishing, 509-520, 1992.
    18. Dorigo M, Optimization, “learning and natural algorithms” PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992 [in Italian].
    19. Dorigo M, Gambardella LM, “Ant colony system: A cooperative learning approach to the traveling salesman problem”, IEEE Trans-actions on Evolutionary Computing, 1997, 1(1), 53–66.
    20. Dorigo M, Stützle T, “Ant Colony optimization. Cambridge, MA: MIT Press; 2004.
    21. Bullnheimer B, Hartl R, Strauss C, “A new rank-based version of the Ant System: A computational study”, Central European Journal of Operations Research, 1999;7(1):25–38.
    22. Stützle T, Hoos HH, “MAX–MIN Ant system”, Future Generation Computer Systems 2000, 16(8), 889–914.
    23. Blum C, Dorigo M, “The hyper-cube framework for ant colony op-timization”, IEEE Transactions on System Management Cybernetics Part B 2004, 34(2), 1161–72.
    24. Gambardella LM, Taillard ÉD, Agazzi G, “MACS-VRPTW: A mul-tiple ant colony system for vehicle routing problems with time win-dows”, “New ideas in optimization”, London: McGraw-Hill; 1999. p. 63–76.
    25. Reimann M, Doerner K, Hartl RF, “D-ants: Savings based ants di-vide and conquer the vehicle routing problems”, Computers & Op-erations Research, 2004, 31(4), 563–91.
    26. Kennedy, J. and Eberhart, R. C, “Particle swarm optimization”, Proceedings IEEE International conference on neural networks Vol. 4, pp. 1942-1948. IEEE service center, Piscataway, NJ, 1995.
    27. Eberhart, R. C. and Kennedy J, “A new optimizer using particle swarm theory”, Proceedings of the sixth international symposium on micro machine and human science pp. 39-43. IEEE service center, Piscataway, NJ, Nagoya, Japan, 1995.
    28. Eberhart, R. C. and Shi, “Particle swarm optimization: develop-ments, applications and resources”, Proc. congress on evolutionary computation 2001 IEEE service center, Piscataway, NJ., Seoul, Ko-rea., 2001.
    29. Y amilledel V alle, Ganesh Kumar Venayagamoorthy, Salman Mo-hagheghi, Jean-Carlos Hernandez, Ronald G. Harley, “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems”, IEEE Transactions on Evolutionary Computation, 12(2), April 2008.
    30. D. Karaboga, “An idea based on honey bee swarm for numerical optimization”, Technical Report-TR06, Erciyes University, Engi-neering Faculty, Computer Engineering Department, 2005.
    31. Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu, “Enhanced Artificial Bee Colony Optimization”, International Jour-nal of Innovative Computing, Information and Control Volume 5, Issue 12, December 2009.
    32. Karaboga, Dervis, and BahriyeAkay, "A modified artificial bee col-ony (ABC) algorithm for constrained optimization prob-lems." Applied soft computing, 11(3), April 2011, 3021-3031.
    33. Karaboga, D., Gorkemli, B., Ozturk, C., &Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57.
    34. Xin-She Yang, "Firefly algorithms for multimodal optimiza-tion", International symposium on stochastic algorithms. Springer, Berlin, Heidelberg, Oct 2009, 168-178.
    35. S. Palit, S. Sinha, M. Molla, A. Khanra, M. Kule, A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm, in: Computer and Communication Technology (ICCCT), 2011 2nd International Conference on, IEEE, 2011, pp. 428–432.
    36. R. Falcon, M. Almeida, A. Nayak, Fault identification with binary adaptive fireflies in parallel and distributed systems, in: Evolution-ary Computation (CEC), 2011 IEEE Congress on, IEEE, 2011, pp. 1359–1366.
    37. S. M. Farahani, A. Abshouri, B. Nasiri, M. Meybodi, A gaussian firefly algorithm, International Journal of Machine Learning and Computing 1 (5) (2011) 448–454.
    38. X. S. Yang, Metaheuristic optimization: algorithm analysis and open problems, in: P. Pardalos, S. Rebennack (Eds.), Experimental Algo-rithms, Lecture notes in computer science, Vol. 6630, Springer Ver-lag, Berlin, 2011, pp. 21–32.
    39. L. dos Santos Coelho, D. L. de Andrade Bernert, V. C. Mariani, A chaotic firefly algorithm applied to reliability-redundancy optimiza-tion, in: Evolutionary Computation (CEC), 2011 IEEE Congress on, Vol. 18, IEEE, 2013, pp. 89–98.
    40. A. Gandomi, X.-S. Yang, S. Talatahari, A. Alavi, Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation 18 (1) (2013) 89 – 98. doi:10.1016/j.cnsns.2012.06.009.
    41. M. Subutic, M. Tuba, N. Stanarevic, Parallelization of the firefly algorithm for unconstrained optimization problems, in: Latest Ad-vances in Information Science and Applications, 2012, pp. 264–269.
    42. A. Husselmann, K. Hawick, Parallel parametric optimisation with firefly algorithms on graphical processing units, Technical Report CSTN-141.
    43. T. Apostolopoulos, A. Vlachos, Application of the firefly algorithm for solving the economic emissions load dispatch problem, Interna-tional Journal of Combinatorics 2011 (2011).
    44. X. S. Yang, S. S. S. Hosseini, A. H. Gandomi, Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect, Applied Soft Computing 12 (3) (2011) 1180–1186
    45. B. Rampriya, K. Mahadevan, S. Kannan, Unit commitment in de-regulated power system using lagrangian firefly algorithm, in: Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on, IEEE, 2010, pp. 389–393.
    46. Y. Zhang, L. Wu, A novel method for rigid image registration based on firefly algorithm, International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC) 2 (2) (2012) 141–146.
    47. M. Horng, Vector quantization using the firefly algorithm for image compression, Expert Systems with Applications 39 (1) (2012) 1078–1091.
    48. T. Hassanzadeh, H. Vojodi, F. Mahmoudi, Non-linear grayscale im-age enhancement based on firefly algorithm, in: Swarm, Evolution-ary, and Memetic Computing, Springer, 2011, pp. 174–181.
    49. B. Basu, G. Mahanti, Fire-fly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna, Pro-gress In Electromagnetics Research B 32 (2011) 169–190.
    50. B. Jakimovski, B. Meyer, E. Maehle, Firefly flashing synchroniza-tion as inspiration for self-synchronization of walking robot gait pat-terns using a decentralized robot control architecture, Architecture of Computing Systems (ARCS) 2010 (2010) 61–72.
    51. S. Severin, J. Rossmann, A comparison of different metaheuristic algorithms for optimizing blended ptp movements for industrial ro-bots, Intelligent Robotics and Applications (2012) 321–330.
    52. Fister, Iztok, Xin-She Yang, and Janez Brest. "A comprehensive review of firefly algorithms." Swarm and Evolutionary Computa-tion 13 (2013): 34-46.
  • Downloads

  • How to Cite

    Gireesha. B, M., & ., . (2018). A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques. International Journal of Engineering and Technology, 7(4.5), 455-458. https://doi.org/10.14419/ijet.v7i4.5.20205