CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Nature-Inspired Algorithms: State-of-Art, Problems and Prospects

by Parul Agarwal, Shikha Mehta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 14
Year of Publication: 2014
Authors: Parul Agarwal, Shikha Mehta
10.5120/17593-8331

Parul Agarwal, Shikha Mehta . Nature-Inspired Algorithms: State-of-Art, Problems and Prospects. International Journal of Computer Applications. 100, 14 ( August 2014), 14-21. DOI=10.5120/17593-8331

@article{ 10.5120/17593-8331,
author = { Parul Agarwal, Shikha Mehta },
title = { Nature-Inspired Algorithms: State-of-Art, Problems and Prospects },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 14 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number14/17593-8331/ },
doi = { 10.5120/17593-8331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:57.622823+05:30
%A Parul Agarwal
%A Shikha Mehta
%T Nature-Inspired Algorithms: State-of-Art, Problems and Prospects
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 14
%P 14-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nature-inspired algorithms have gained immense popularity in recent years to tackle hard real world (NP hard and NP complete) problems and solve complex optimization functions whose actual solution doesn't exist. The paper presents a comprehensive review of 12 nature inspired algorithms. This study provides the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of required input parameters, their key evolutionary strategies and application areas. A list of automated toolboxes available for directly evaluating these nature inspired algorithms over numerical optimization problems indicates the need for unified toolbox for all nature inspired algorithms. It also elucidates the users with the minimum and maximum dimensions over which these algorithms have already been evaluated on benchmark test functions. Hence this study would aid the research community to know what all algorithms could be examined for large scale global optimization to overcome the problem of 'curse of dimensionality'.

References
  1. M. Molga and C. Smutnicki, "Test functions for optimization needs", kwietnia, 2005.
  2. I. Fister, X. S. Yang, J. Brest, and D. Fister, "A Brief Review of Nature-Inspired Algorithms for Optimization", Elektrotehni?Ski Vestnik, Jul, 2013.
  3. H. Banati and S. Mehta, "SEVO: Bio-inspired Analytical Tool for Unimodal and Multimodal Optimization", International Conference on Soft Computing for Problem Solving, Advances in Intelligent and Soft Computing, Springer, 2011, pp. 557-566.
  4. H. Pohlheim, "Genetic and Evolutionary Algorithm Toolbox (GEATbx) for Use with Matlab" http://www/geatbx. com, 1998.
  5. http://www. mathworks. com/matlabcentral/fileexchange/7506
  6. K. C. Tan, T. H. Lee, D. Khoo, and E. F. Khor, "A Multiobjective Evolutionary Algorithm Toolbox for Computer-Aided Multiobjective Optimization", IEEE transactions on systems, man and cybernetics-PartB: cybernetics, Vol. 31, No. 4, 2001.
  7. Toolbox - Ant Colony Optmization , Available: http://www. lbic. free. unicamp. br/homepage/downloads/acoPOR. htm
  8. ABC Algorithm Information, Available: http://mf. erciyes. edu. tr/abc/software. htm
  9. Nature Inspired MATLAB based Toolbox!, Available: http://www. drsatvir. in/tools. php#
  10. E. Elbeltagi, T. Hegazy and D. Grierson, "Comparison among five evolutionary-based optimization algorithms", Advanced Engineering Informatics, Elsevier, 2005, pp. 43–53.
  11. X. S. Yang, "Nature- Inspired Mateheuristic Algorithms: Success and New Challenges", J Comput. Eng. Inf. Technol. , Vol. 1, Issue 1, 2012, pp. 1-3.
  12. X. S. Yang, Z. Cue, R. Xiao, A. H. Gandomi et al. , "Swarm Intelligence and Bio-Inspired computation, Theory and Applications", Elsevier, Waltham, Mass, USA, 2013.
  13. D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm", Applied Soft Computing, Elsevier, 2007, pp. 687-697.
  14. D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, Springer, 2007, pp. 459-471.
  15. K. M. Passino, "Biomimicry of Bacterial Foraging for Distributed Optimization and Control", IEEE Control Systems Magazine, 2006, pp. 52-67.
  16. X. S. Yang, "Firefly Algorithm, Stochastic Test Functions and Design Optimisation", Int. J. Bio-Inspired Computation, Vol. 2, No. 2, 2010, pp. 78–84.
  17. X. S. Yang and X. He, "Firefly Algorithm: Recent Advances and Applications", Int. J. Swarm Intelligence, Vol. 1, No. 1, 2013, pp. 36–50.
  18. X. S. Yang, "Bat algorithm: literature review and applications", Int. J. Bio-Inspired Computation, Vol. 5, No. 3, 2013, pp. 141–149.
  19. X. S. Yang and S. Deb, "Engineering Optimisation by Cuckoo Search", Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, 2010, pp. 330–343.
  20. J. G. Digalakis and K. G. Margaritison, "Benchmarking Functions for Genetic Algorithms", Intern. J. Computer Math. , Vol. 00, 2000, pp. 1-27.
  21. DE. Goldberg, "Genetic algorithms in search, optimization and machine learning", Reading, MA, Addison-Wesley, 1989.
  22. P. Moscato and MG. Norman, "A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems", International conference on parallel computing and transputer application, 1992, pp. 177–86.
  23. J. Kennedy and R. Eberhart, "Particle swarm optimization", IEEE international conference on neural networks, 1995, pp. 1942–1948.
  24. M. Dorigo, V. Maniezzo and A. Colorni, "Ant system: optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 26, No. 1, 1996, pp. 1-13.
  25. M. Eusuff, K. Lansey and F. Pasha, "Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization", Engineering Optimization, Vol. 38, No. 2, 2006, pp. 129-154.
  26. D. Simon, "Biogeography-Based Optimization", IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, DEC 2008, pp. 702-713.
  27. V. Kadirkamanathan, "Bayesian Inference for Basis Function Selection in Nonlinear System Identification using Genetic Algorithms", Maximum Entropy and Bayesian Methods, Fundamental Theories of Physics, Vo. 70, 1996, pp. 135-142.
  28. X. S. Yang, "Flower pollination algorithm for global optimization", Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, Vol. 7445, 2012, pp. 240–249.
  29. X. S. Yang, M. Karamanoglu and X. He, "Multi-objective Flower Algorithm for Optimization", International Conference on Computational Science, Elsevier Science, Vol. 18, 2013, pp. 861-868.
  30. A. Parashar and K. K. Swankar, "Genetic algorithm using to the solution of unit commitment", International Journal of Engineering Trends and Technology, Vol. 4, No. 7, 2013, pp. 2986-2990.
  31. K. Tang, X. Yao, P. N. Suganthan, C. MacNish, et al. "Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization", Technical Report, University of Science and Technology of China, 2008.
  32. D. Karaboga and B. Akay, "An artificial bee colony (abc) algorithm on training artificial neural networks", 15th IEEE Signal Processing and Communications Applications, SIU, June 2007, pp. 1–4.
  33. D. Karaboga, C. Ozturk and B. Akay, "Training neural networks with ABC optimization algorithm on medical pattern classification", International Conference on Multivariate Statistical Modelling and High Dimensional Data Mining, TURKEY, June 2008.
  34. C. Ozturk and D. Karaboga, "Classification by neural networks and clustering with artificial bee colony (ABC) algorithm", Sixth International Symposium on Intelligent and Manufacturing Systems Features, Strategies and Innovation Turkiye, October 2008.
  35. L. Fenglei, D. Haijun and F. Xing, "The parameter improvement of bee colony algorithm in TSP problem", Science Paper Online, November 2007.
  36. A. Singh, "An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem", Applied Soft Computing, Vol. 9, No. 2, 2008, pp. 625–631.
  37. D. H. Kumar, P S. Babu and M P. Lalitha, "Distribution System Network Reconfiguration by using Artificial Bee Colony Algorithm", IOSR Journal of Electrical and Electronics Engineering, Vol. 9, No. 1, Jan. 2014, pp. 48-52.
  38. X. S. Yang and S. Deb, "Cuckoo search: recent advances and applications," Neural Computing and Applications, vol. 24, no. 1, 2014, pp. 169–174.
  39. Nitesh Sureja, "New Inspirations in Nature: A Survey", International Journal of Computer Applications & Information Technology, Vol. 1, No. 3, November 2012, pp. 21-24.
  40. S. Binitha and S. S. Sathya, "A Survey of Bio inspired Optimization Algorithms, International Journal of Soft Computing and Engineering, Vol. 2, No. 2, May 2012, pp. 137-151.
  41. 2014 IEEE Congress on Evolutionary Computation (CEC), Available: http://www. ieee. org/conferences_ events/conferences/conferencedetails/index. html?Conf_ID=32438
  42. C Wu, N Zhang, J Jiang, J Yang and Y Liang, "Improved Bacterial Foraging Algorithms and Their Applications to Job Shop Scheduling Problems", 8th International Conference, ICANNGA 2007, Springer Berlin Heidelberg, Vol. 4431, 2007, pp. 562-569.
  44. R Jakhar, N Kaur and R Singh, "Face Recognition Using Bacteria Foraging Optimization-Based Selected Features", International Journal of Advanced Computer Science and Applications, pp. 106-111.
  45. L Tan, H Wang, X Liang and K Xing, "An Adaptive Comprehensive Learning Bacterial Foraging Optimization for Function Optimization", Communications in Computer and Information Science, Vol. 375, 2013, pp. 194-199.
  46. O. P. Verma, R. Sharma and D. Kumar, "Binarization based Image Edge Detection using Bacterial Foraging Algorithm", 2nd International Conference on Communication," Elsevier, Vol. 6, 2012, pp. 315–323.
  47. Beenu, S. Kaur, "Image Segmentation using Improved Bacterial Foraging Algorithm", International Journal of Science and Research, Vol. 2, No. 1, 2013, pp. 63-69.
  48. R. Kaur, A. Girdhar and S Gupta, "Color Image Quantization based on Bacteria Foraging Optimization", International Journal of Computer Applications, Vol. 25, No. 7, 2011, pp. 33-42.
  49. R. Lourenço and D. Serra, "Adaptive search heuristics for the generalized assignment problem", Mathware & soft computing, vol. 9, no. 2-3, 2002.
  50. R. Hadji, M. Rahoual, E. Talbi and V. Bachelet, "Ant colonies for the set covering problem", Abstract proceedings of ANTS2000, pp. 63-66.
  51. D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens, "Classification with Ant Colony Optimization", IEEE Transactions on Evolutionary Computation, Vol. 11, No. 5, 2003, pp. 651-665.
  52. G. D. Caro and M. Dorigo, "Extending AntNet for best-effort quality-of-service routing," Proceedings of the First Internation Workshop on Ant Colony Optimization (ANTS'98), 1998.
  53. S. Fidanova, "ACO algorithm for MKP using various heuristic information", Numerical Methods and Applications, vol. 2542, 2003, pp. 438-444.
  54. S. Gupta , K. Bhuchar and P. S. Sandhu, "Implementing Color Image Segmentation Using Biogeography Based Optimization" , International Conference on Software and Computer Applications, vol. 9, 2011, pp. 79-86.
  55. B. Zhao, C. Den, Y. Yang and H. Peng, "Novel Binary Biogeography-Based Optimization Algorithm for the Knapsack Problem", 2012, pp. 217–224.
  56. V. K. Panchal, "Biogeography based Satellite Image Classification", International Journal of Computer Science and Information Security, Vol. 6, No. 2, 2009.
  57. M. H. Horng, "Vector quantization using the firefly algorithm for image compression", Expert Systems with Applications, 2012, pp. 1078-1091.
  58. H. Banati and M. Bajaj, "Firefly based feature selection approach", Int. J. Computer Science Issues, Vol. 8, No. 2, 2011, pp. 473-480.
  59. S. Nandy, P. P. Sarkar and A. Das, "Analysis of nature-inspired firefly algorithm based back-propagation neural network training", Int. J. Computer Applications, Vol. 43, No. 22, 2012, pp. 8-16.
  60. A. Rajini, V. K. David, "A hybrid metaheuristic algorithm for classification using micro array data", Int. J. Scientific & Engineering Research, Vol. 3, No. 2, 2012, pp. 1-9.
  61. J. Senthilnath, S. N. Omkar and V. Mani, "Clustering using firely algorithm: performance study", Swarm and Evolutionary Computation, Vol. 1, No. 3, 2011, pp. 164-171.
  62. S. S. Travessa, W. P. Carpes and M. A. Nunes Filho, "Use of an Artificial Neural Network-based metamodel in the optimization by Particle Swarm Optimization method", Universidade Federal de Santa Catarina.
  63. G M. Omran, A. P. Engelbrecht, and A. Salman, "Particle Swarm Optimization Method for Image Clustering, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 19, No. 03, pp. 297-321.
  64. J. Ni, L. Li, F. Qiao, Q. Wu, "A novel memetic algorithm and its application to data clustering", Memetic Computing, March 2013, Vol. 5, No. 1, pp 65-78.
  65. T. Liao, K. Socha, M. A. M. de Oca, Thomas Stutzle, et al. , "Ant Colony Optimization for Mixed-Variable Optimization Problems", IEEE transactions on evolutionary computing, 2013.
  66. M. Dhivya and M. Sundarambal, "Cuckoo search for data gathering in wireless sensor networks", Int J Mobile Communication", 2011, pp. 642–656.
  67. A. Layeb, "A novel quantum-inspired cuckoo search for Knapsack problems", International Journal of Bio-inspired Computation, Vol. 3, No. 5, 2011, pp. 297–305
  68. E. Valian, S. Mohanna and S. Tavakoli, "Improved cuckoo search algorithm for feedforward neural network training" International Journal of Artificial Intelligence Application, Vol. 2, No. 3, 2011, pp. 36–43
  69. G. Komarasamy and A. Wahi, "An optimized K-means clustering technique using bat algorithm", European J. Scientific Research, Vol. 84, No. 2, 2012, pp. 263-273.
  70. M. G. H. Omran, A. P. Engelbrecht and A. Salman, "Particle Swarm Optimization for Pattern Recognition and Image Processing", Swarm Intelligence in Data Mining, Studies in Computational Intelligence, Vol. 34, 2006, pp 125-151.
  71. A. Rahman, E. M. Ahmad and A. R. Akhtar, "A metaheurisic bat inspired algorithm for full body human pose estimation", Ninth Conference on Computer and Robot Vision, 2012, pp. 369–375.
  72. T. A. Lemma, B. M. Hashim, "Use of fuzzy systems and bat algorithm for exergy modelling in a gas turbine generator", IEEE Colloquium on Humanities, Science and Engineering, 2011, pp. 305–310.
  73. Z. Zhijin, Y. Keqiang and Z. Zhidong, "Discrete shuffled frog leaping algorithm for multi-user detection in DS-CDMA communication system," in 2008 11th IEEE International Conference on Communication Technology, ICCT 2008, Hangzhou, China, pp. 421-424, 2008.
  74. Z. Wang and X. Sun, "Image Watermarking Scheme Based on Shuffled Frog Leaping Algorithm", Knowledge Acquisition and Modeling Workshop, IEEE International Symposium on, 2008, pp. 239 – 242.
  75. B. Amiri, M. Fathian and A. Maroosi, "Application of shuffled frog-leaping algorithm on clustering", The International Journal of Advanced Manufacturing Technology, November 2009, Vol. 45, No. 1-2, pp 199-209.
  76. H. Chen, Y. Zhu, and K. Hu, "Adaptive Bacterial Foraging Optimization", Abstract and Applied Analysis, Hindawi, Vol. 2011, pp. 1-27.
  77. S. Mehta and H. Banati, "Improved shuffled frog leaping algorithm for continuous optimization adapted SEVO toolbox", International. J. of Advanced Intelligence Paradigms, Inderscience, Vol. 5, No. 1/2, 2013, pp. 31 – 44.
Index Terms

Computer Science
Information Sciences

Keywords

Nature inspired algorithm key evolutionary strategies automated toolboxes benchmark test function.

Powered by PhDFocusTM