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Parametric Analysis of Nature Inspired Optimization Techniques

by Yugal Kumar, Dharmender Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 3
Year of Publication: 2011
Authors: Yugal Kumar, Dharmender Kumar
10.5120/3888-5442

Yugal Kumar, Dharmender Kumar . Parametric Analysis of Nature Inspired Optimization Techniques. International Journal of Computer Applications. 32, 3 ( October 2011), 42-49. DOI=10.5120/3888-5442

@article{ 10.5120/3888-5442,
author = { Yugal Kumar, Dharmender Kumar },
title = { Parametric Analysis of Nature Inspired Optimization Techniques },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 3 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number3/3888-5442/ },
doi = { 10.5120/3888-5442 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:13.963100+05:30
%A Yugal Kumar
%A Dharmender Kumar
%T Parametric Analysis of Nature Inspired Optimization Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 3
%P 42-49
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are large numbers of the optimization technique that have been used to optimize the thing in the field of computer science, transportation engineering, mechanical engineering, management and so on. But the traditional optimization techniques are replaced by nature inspired techniques. These technique involve directly or indirectly the participation of nature such as GA, ACO, BCO SA, SS. Such techniques provide an abstract way to solve the problem. Each technique is differing from the other technique but each technique having some similarity with other techniques. This paper provides the comparative analysis of Nature inspired optimization techniques in the tabular form.

References
  1. Glover, F. 1989, “Tabu Search — Part I,” ORSA Journal on Computing, Vol. 1, No. 3, pp. 190-206.
  2. Glover, F. 1990, “Tabu Search — Part II,” ORSA Journal on Computing, Vol. 2, No. 1, pp. 4-32.
  3. Battiti, R. and G. Tecchiolli 1994, “The Reactive Tabu Search,” ORSA Journal on Computing, Vol. 6, No. 2, pp. 126-140.
  4. Glover and Laguna 2002, “Tabu search in Pardalos and Resende (eds.), Handbook of Applied Optimization”, Oxford Academic Press.(Book)
  5. Glover, F. 1994 “Tabu Search for Nonlinear and Parametric Optimization with Links to Genetic Algorithms,” Discrete Applied Mathematics journal, vol. 49, pp. 231-255.
  6. Ana I.P.N. Pereira1, Edite M.G.P. Fernandes2 2004,” A Study of Simulated Annealing Variants”, XXVIII Congreso Nacional de Estad±tica e Investigación Operativa SEIO'04 25 a 29 de Octubre de. (conference proceeding)
  7. Dimitris Bertsimas and John Tistsiklis 1993, “Simulated Annealing”, Statistical Sciences, Vol. 8, No. 1, pp 10-15,.
  8. Kirkpatrick, S , Gelatt, C.D. 1983, Vecchi, M.P., ” Optimization by Simulated Annealing” Journal of Science, vol 220, No. 4598, pp 671-680.
  9. Zhao Xinchao 2005 “A Greedy Genetic Algorithm for Unconstrained Global Optimization” Journal of Systems Science and Complexity, Vol. 18 No. 1.
  10. Z. M. Nopiah, M. I. Khairir, S. Abdullah, M. N. Baharin, and A. Arifin 2010, “Time Complexity Analysis of the Genetic Algorithm Clustering Method” , Recent Advances in Signal Processing, Robotics and Automation, ISBN: 978-960-474-157-1.(Conference proceedings)
  11. Davis, L. 1991, “Handbook of Genetic Algorithms”, Van Nostrand Reinhold, New York.
  12. Arturo Chavoya and Yves Duthen 2006, “Using Genetic Algorithim to elove Cellular Automata for 2D/3D Computational Devolopment”, published in 8th annual conference on genetic and evolutionary computation, ISBN : 1-59593-186-4.(conference procedings)
  13. Nada M.A. AL-Salami 2009, “Evolutionary Algorithm Definition”, American J. of Engineering and Applied Sciences 2 (4): pp 789-795.
  14. Poli, R., W.B. Langdon, N.F. McPhee and J.R. Koza 2007, “Genetic programming: An introductory tutorial and a survey of techniques and applications” Technical report CES-475, http://citeseerx.ist.psu.edu/viewdoc/ summary? doi= 10.1.1.126.3889. (Technical Report)
  15. Spector, L. and Stoffel K. 1996, “Onto genetic programming”, Proceedings of the 1st Annual Conference, MIT Press, Stanford University, CA., USA., pp: 394-399,.(conference proceeding)
  16. Koza, J.R. 1995,”Survey of genetic algorithms and genetic programming”, Proceeding of the Conference on Microelectronics Communications Technology Producing Quality Products Mobile and Portable Power Emerging Technologies, Nov. 7-9, IEEE Xplore Press, San Francisco, CA, USA., pp: 589.(conference proceeding)
  17. Fabian Landis 2004, “3D Model Optimization Using Evolutionary Algorithms”. (Thesis)
  18. J. Santamarfa, O. Cordon, S. Damas, I. Aleman, and M. Botella 2006, “3D Forensic Model Reconstruction by Scatter Search-based Pair-wise Image Registration”, IEEE International Conference on Fuzzy Systems Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July pp 16-21.(conference proceeding)
  19. F. Glover, M. Laguna, and R. Mart´I 2003, “Scatter search”, Theory and Applications of Evolutionary Computation: Recent Trends, Edited by A. Ghosh and S. Tsutsui, pp. 519–537, Springer-Verlag,.
  20. F. Herrera , M. Lozano, D. Molina 2006,” Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies”, European Journal of Operational Research 169 ,pp 450–476.
  21. Laguna, M., Martí R. 2000 “Experimental Testing of Advanced Scatter Search Designs for Global Optimization of Multimodal Functions,” TR11-2000.( Technical Report)
  22. Rafael Martı, Manuel Laguna 2006, Fred Glover,” Principles of scatter search”, European Journal of Operational Research, Published by Elsevier vol. no. 169, pp 359-372.
  23. M. Ranjbar and F. Kianfar 2009, “A Hybrid Scatter Search for the RCPSP”, Transaction E: Industrial Engineering Vol. 16, No. 1, pp. 11-18.
  24. K. Shimizu,S. Ito and S. Suzuki 2005, “Tracking Control of General Nonlinear Systems by a Direct Gradient Descent Method”, Nonlinear Dynamics and Systems Theory, 5(1), pp 91–105.
  25. Paul Tseng and Sangwoon Yun 2010, “A Coordinate Gradient Descent Method for Linearly Constrained Smooth Optimization and Support Vector Machines Training” Computational optimization and applications Vol. 47, No. 2. pp. 179-206.
  26. Xu Wang 2008, “Method of Steepest Descent and its Applications”, in IEEE Microwave and Wireless Components Letters Volume 12. pp 24-26. ISSN: 15311309.
  27. G.V. Deklaits 1983, A. Ravindran and K.M. Rogesdell, “Engineering optimization Methods and Application”, a willey interscience publication.
  28. J. Barzilai and J. Borwein 1988. Two-point step size gradient methods. IMA Journal of Numerical Analysis, 8, pp 141–148.
  29. Walter J. Gutjahr 2008, “First steps to the runtime complexity analysis of ant colony optimization”, Computers & Operations Research Volume 35, Issue 9, pp 2711-2727.
  30. Christian Blum 2005, “Ant colony optimization: Introduction and recent trends” , Physics of Life Reviews 2, pp 353–373.
  31. Dorigo M, Maniezzo V, Colorni A 1996. Ant System: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybernet Part B, 26(1):29–41.
  32. Marco Dorigo, Christian Blum 2005,” Ant colony optimization theory: A survey”, Theoretical Computer Science 344 published by Elsevier, 243 – 278.
  33. L.M. Gambardella, E.D. Taillard, G. Agazzi 1999, “MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows”, New Ideas in Optimization, McGraw-Hill, London, UK, pp. 63–76. (Book)
  34. Teodorovic, Dusan Lucic, Panta Markovic, Goran Orco, Mauro Dell 2006, “Bee Colony Optimization: Principles and Applications”, Neural Network Applications in Electrical Engineering, pp 151 - 156, ISBN: 1-4244-0433-9. (seminar report)
  35. Low, M.Y.H. Chandramohan, M. Chwee Seng Choo 2009, “Application of multi-objective bee colony optimization algorithm to Automated Red Teaming”, on page no. 1798 – 1808 appears in Winter Simulation Conference (WSC), (Conference proceeding)
  36. D. Karaboga and B. Akay 2009, “Artificial bee colony (abc), harmony search and bees algorithms on numerical optimization,” in Innovative Production Machines and Systems Virtual Conference (IPROMS 2009), available at: http://conference.iproms.org/sites/conference. iproms.org/ files/IPROMSABCv2.pdf. (conference proceeding)
  37. Teodorovic, D., Dell’Orco M. September 2005, Bee colony optimization—a cooperative learning approach to complex transportation problems, Proceedings of the 10th EWGT Meeting, Poznan, pp 13-16,. (proceeding)
  38. A. Baykasoglu, L. Ozbakir, and P. Tapkan, 2007, “Artificial bee colony algorithm and its application to generalized assignment problem,” in Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, F. T. Chan and M. K. Tiwari, Eds. Vienna, Austria: Itech Education and Pub., pp. 113–144, ISBN 978-3-902613-09-7.
  39. Dusan Teodorovi, Tatjana Davidovi and Milica Selmi, “Bee Colony Optimization: The Applications Survey”, ACM Transactions on Computational Logic.(under publication)
  40. A. K. M. Khaled Ahsan Talukder, Michael Kirley and Rajkumar Buyya 2009, “Multi-objective differential evolution for scheduling workflow applications on global Grids”, Concurrency and Computation: Practice and Experience Published online in Wiley Inter Science,.
  41. Jeyakumar, G. Velayutham, C.S Dec. 2009, “An empirical comparison of Differential Evolution variants on different classes of unconstrained global optimization problems”, Nature & Biologically Inspired Computing, page no. 866 - 871,.( World Congress conference computing)
  42. Ricardo Landa Becerra and Carlos A. Coello Coello, 2006” Cultured differential evolution for constrained optimization”, Computer Methods in Applied Mechanics and Engineering ,Volume 195, Issues 33-36, Pages 4303-4322.
  43. Bo Ping Wang and Chirag N Patel, September 2002, “Differential Evolution for Design Optimization”, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, Georgia. (Symposium proceeding)
  44. Josef Tvrdık 2007, “Adaptive Differential Evolution: Application to Nonlinear Regression”, Proceedings of the International Multi conference on Computer Science and Information Technology pp. 193–202, ISSN 1896-7094,.(conference proceeding)
  45. R. Storn and K. V. Price Dec 1997.. “Differential Evolution - a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 11(4):341-359,
  46. Swagatam Das and Ponnuthurai Nagaratnam Suganthan 2011, “Differential Evolution: A Survey of the State-of-the-Art”, IEEE Transactions on Evolutionary Computation, IEEE Trancations on Evolutionary Computation vol 15, no. 1.
  47. David Sedigizadeh and Masehain Ellips 2009, “Particle Swarm Optimization Method, Taxonomy and Applications”, International Journal of Computer Theory and Engg., vol. 1,no. 5, 1793-1802.
  48. Yi Da and Ge Xiurun 2005, “An improved PSO-based ANN with Simulated Annealing Technique”, Journal Neurocomputing vol. 63, page no. 527–533.
  49. Carlisle, A. and Dozier, G 2000 “ Adaptive Particle Swarm Optimization to Dynamic Environment.” Proc of International Conference n Artificial Intelligence. pp.429-434. (conference proceeding)
  50. J. Kennedy 1997, “The particle swarm: social adaptation of knowledge”, Proceedings of the 1997 International Conference on Evolutionary Computation, pp. 303–308, Indianapolis, Indiana, USA (conference proceeding)
  51. Shi, Y. and Eberhart, R. C. 1999, “Empirical study of particle swarm optimization” Proceedings of the Congress on Evolutionary Computation. (conference proceeding)
  52. Sharaf, A.M.; El-Gammal 2009, A.A.A., “A discrete particle swarm optimization technique (DPSO) for power filter design”, 4th international Design and Test Workshop (IDT), page no. 1-6 . (workshop proceeding)
  53. V.Selvi and Dr. R.Umarani 2010, “Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques’, International Journal of Computer Applications (0975 – 8887) Volume 5– No.4.
  54. Clarc, M and Kennedy, J, 2002, “The particle swarm – explosion, stability, and convergence in a multidimensional complex space”, IEEE Transactions on Evolutionary Computation. Vol 6 no. 1 pp. 58-73.
  55. Bohachevsky, I. O. 1986, Johnson, M. E. and Stein, M. L.,Generalized “Simulated Annealing for Function Optimization” Technometrics vol. 28, no. 3, pp 209-217.
  56. Pietro S. Oliveto Jun He¤ Xin Yao 2007,” Time Complexity of Evolutionary Algorithms for Combinatorial Optimization: A Decade of Results”, International Journal of Automation and Computing, vol 03 no. 4 pp 281-293.
  57. Dorigo, V. Maniezzo, and A. Colorni 1991, The ant system: an autocatalytic optimizing process, Technical Report TR91-016, Politecnico di Milano (Technical Report)
  58. Zulkifli Zainal Abidin, Mohd Rizal Arshad, Umi Kalthum Ngah 2009, “A Survey: Animal-Inspired Metaheuristic Algorithms”, Proceedings of the Electrical and Electronic Postgraduate Colloquium EEPC2009.(proceeding)
  59. Qinghai Bai 2010,” Analysis of Particle Swarm Optimization Algorithm”, journal of computer and information science, vol.3, no. 1,pp 180-184.
  60. James Kennedy and Russell Eberhart 1995,”Particle Swarm Optimization”, in proceeding of IEEE International Conference of Neural Network, pp 1942-1948. (conference proceeding)
Index Terms

Computer Science
Information Sciences

Keywords

Optimization Techniques Stochastic Population Heuristic

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