On the performances of the flower pollination algorithm - Qualitative and quantitative analyses

Applied Soft Computing Journal

Volumn 34, Issue , 2015, Pages 349-371

  Draa, Amer ^a  

^a University Constantine 2   (Algeria)

Author keywords

Continuous optimisation; Flower pollination algorithm; Modified equation; Opposition based learning; Real world benchmarks; Statistical analysis

Indexed keywords

ALGORITHMS; CHEMICAL ANALYSIS; STATISTICAL METHODS;

CONTINUOUS OPTIMISATION; META-HEURISTIC OPTIMISATION; MODIFIED EQUATION; OPPOSITION-BASED LEARNING; OPTIMISATION PROBLEMS; QUALITATIVE AND QUANTITATIVE ANALYSIS; REAL-WORLD; STATE-OF-THE-ART ALGORITHMS;

OPTIMIZATION;

EID: 84930937520     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.05.015     Document Type: Article

References (83)

1. W.J. Gutjahr Convergence analysis of metaheuristics Matheuristics: Annals of Information Systems 2010 Springer USA 159 187
2. J. Holland Adaptation in Natural and Artificial Systems 1992 MIT Press Cambridge, MA
3. R. Storn, and K. Price Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces Technical Rep. No. TR-95-012 1995 International Computer Science Institute Berkley, CA
4. R. Storn On the usage of differential evolution for function optimization Proceedings of the Biennial Conference of the North American Fuzzy Information Processing Society 1996 519 552
5. R. Storn, and K. Price Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces J. Glob. Optim. 11 1997 341 359
6. J. Kennedy, and R. Eberhart Particle swarm optimization Proceedings of IEEE International Conference on Neural Networks IV 1995 1942 1948
7. Y. Shi, and R.C. Eberhart A modified particle swarm optimizer Proceedings of IEEE International Conference on Evolutionary Computation 1998 69 73
8. J. Kennedy The particle swarm: social adaptation of knowledge Proceedings of IEEE International Conference on Evolutionary Computation 1997 303 308
9. A. Colorni, M. Dorigo, and V. Maniezzo Distributed optimization by ant colonies Proceedings of the First European Conference on Artificial Life 1992 MIT Press Cambridge, MA 134 142
10. A. Colorni, M. Dorigo, and V. Maniezzo An investigation of some properties of an ant algorithm Proceedings of PPSN-II, Second International Conference on Parallel Problem Solving from Nature 1992 509 520
11. A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian Ant system for job-shop scheduling Belgian J. Oper. Res. Stat. Comput. Sci. 34 1 1994 39 53
12. X.S. Yang Nature-Inspired Metaheuristic Algorithms 2008 Luniver Press Beckington, UK
13. X.S. Yang Firefly algorithm, stochastic test functions and design optimization Int. J. Bio-Inspir. Comput. 2 2 2010 78 84
14. X.S. Yang A new metaheuristic bat-inspired algorithm Nature Inspired Cooperative Strategies for Optimization, (NISCO 2010), Studies in Computational Intelligence 2010 Springer Berlin, 284, Springer 65 74
15. X.S. Yang, and S. Deb Cuckoo search via Levy flights World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) 2009 IEEE Publications 210 214
16. X.S. Yang, and S. Deb Cuckoo search: recent advances and applications Neural Comput. Appl. 24 1 2014 169 174
17. X.S. Yang, and S. Deb Eagle strategy using Levy walk and firefly algorithms for stochastic optimization Nature Inspired Cooperative Strategies for Optimization, (NISCO 2010), vol. 284 2010 101 111
18. X.S. Yang Flower pollination algorithm for global optimization Unconventional Computation and Natural Computation 2012 Springer Berlin 240 249
19. M. Črepinšek, S.H. Liu, and M. Mernik Exploration and exploitation in evolutionary algorithms: a survey ACM Comput. Surv. 45 3 2013 35
20. J.J. Liang, B.Y. Qu, P.N. Suganthan, and A.G. Hernndez-Daz Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization, Zhengzhou University and Nanyang Technological University, Zhengzhou China and Singapore, Tech. Rep. 2012
21. S. Das, and P.N. Suganthan Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems, Technical Report, Jadavpur University, Nanyang Technological University 2010
22. X.S. Yang, M. Karamanoglu, and X. He Multi-objective flower algorithm for optimization Proc. Comput. Sci. 18 2013 861 868
23. X.S. Yang, M. Karamanoglu, and X. He Flower pollination algorithm: a novel approach for multiobjective optimization Eng. Optim. 2013 1 16
24. X.S. Yang, Z. Cui, R. Xiao, A.H. Gandomi, and M. Karamanoglu Swarm intelligence and bio-inspired computation, theory and application Elsevier Science & Technology Books 2013
25. D. Rodrigues, X.S. Yang, A.N. Souza, and J.P. Papa Binary flower pollination algorithm and its application to feature selection Recent Adv. Swarm Intell. Evolut. Comput. Stud. Comput. Intell. 585 2015 85 100
26. I. Pavlyukevich Levy flights, non-local search and simulated annealing J. Comput. Phys. 226 2007 1830 1844
27. M.M. Mulya Pambudy, S.P. Hadi, and H.R. Ali Flower pollination algorithm for optimal control in multi-machine system with GUPFC 6th International Conference on Information Technology and Electrical Engineering (ICITEE) 2014 1 6
28. M. Sharawi, E. Emary, I. Aly Saroit, and H. El-Mahd Flower pollination optimization algorithm for wireless sensor network lifetime global optimization Int. J. Soft Comput. Eng. 4 3 2014 54 59
29. A. Ochoa, S. Gonzalez, L. Margain, T. Padilla, O. Castillo, and P. Melin Implementing flower multi-objective algorithm for selection of university academic credits Nat. Biol. Inspir. Comput. (NaBIC) 2014 7 11
30. R. Prathiba, M. Balasingh Moses, and S. Sakthivel Flower pollination algorithm applied for different economic load dispatch problems Int. J. Eng. Technol. 6 2 2014 1009 1016
31. O. Abdel-Raouf, M. Abdel-Baset, and I. El-Henawy A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles Int. J. Eng. Trends Technol. 7 3 2014 126 132
32. I. El-henawy, and M. Ismail An improved chaotic flower pollination algorithm for solving large integer programming problems Int. J. Digit. Content Technol. Appl. 8 3 2014 72 81
33. O. Abdel-Raouf, M. Abdel-Baset, and I. El-henawy An improved flower pollination algorithm with chaos Int. J. Educ. Manag. Eng. 4 2 2014 1 8
34. D. Chakraborty, S. Saha, and O. Dutta DE-FPA, a hybrid differential evolution-flower pollination algorithm for function minimization High Perform. Comput. Appl. (ICHPCA'14) 2014 1 6
35. R. Wang, and Y. Zhou Flower pollination algorithm with dimension by dimension improvement Math. Probl. Eng. 2014 2014 1 9
36. K. Balasubramani, and K. Marcus A study on flower pollination algorithm and its applications Int. J. Appl. Innov. Eng. Manag. 3 11 2014 230 235
37. N.UI Sakib, M.W. Kabir, Md. Subbir Rahman, and M.S. Alam A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems Int. J. Appl. Inf. Syst. 7 9 2014 19 20
38. S. Lukasik, and P.A. Kowalski Study of flower pollination algorithm for continuous optimization IEEE Conf. Intell. Syst. 2014 451 459
39. P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.P. Chen, A. Auger, and S. Tiwari Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization KanGAL Report 2005 IIT Kanpur India
40. J. Derrac, S. Garcia, D. Molina, and F. Herrera A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms Swarm Evol. Comput. 1 2011 3 18
41. J. Derrac, S. Garcia, S. Hui, P.N. Suganthan, and F. Herrera Analyzing convergence performance of evolutionary algorithms: a statistical approach Inf. Sci. 289 2014 41 58
42. N. Veček, M. Mernik, and M. Črepinšek A chess rating system for evolutionary algorithms: a new method for the comparison and ranking of evolutionary algorithms Inf. Sci. 277 2014 656 679
43. M. Črepinšek, S.H. Liu, L. Mernik, and M. Mernik Is a comparison of results meaningful from the inexact replications of computational experiments Soft Comput. 2015 (in press)
44. Z.W. Geem, J.H. Kim, and G. Loganathan A new heuristic optimization algorithm: harmony search Simulation 76 2001 60 68
45. M. Mernik, S.H. Liu, D. Karaboga, and M. Črepinšek On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation Inform. Sci. 229 2015 115-127, 215
46. G. Iacca, F. Caraffini, and F. Neri Multi-strategy coevolving aging particle optimization Int. J. Neural Syst. 24 1 2014
47. S. Holm A simple sequentially rejective multiple test procedure Scand. J. Stat. 6 2 1979 65 70
48. S. Garcia, A. Fernandez, J. Luengo, and F. Herrera A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability Soft Comput. 13 10 2008 959 977
49. F. Caraffini, G. Iacca, F. Neri, L. Picinali, and E. Mininno A CMA-ES super-fit scheme for the re-sampled inheritance search Proceedings of the IEEE International Conference on Evolutionary Computation 2013 1123 1130
50. A. Draa, S. Bouzoubia, and I. Boukhalfa A sinusoidal differential evolution algorithm for numerical optimisation Appl. Soft Comput. 27 2015 99 126
51. F. Caraffini, F. Neri, J. Cheng, G. Zhang, L. Picinali, G. Iacca, and E. Mininno Super-fit multicriteria adaptive differential evolution Proceedings of the 2013 IEEE Congress on Evolutionary Computation 2013 1678 1685
52. I. Poikolainen, and F. Neri Differential evolution with concurrent fitness based local search Proceedings of the IEEE Congress on Evolutionary Computation 2013 384 391
53. J. Tvrdik, and R. Polakova Competitive differential evolution applied to CEC 2013 problems Proceedings of the IEEE Congress on Evolutionary Computation 2013 1651 1657
54. S. Biswas, S. Kundu, S. Das, and V. Vasilakos Teaching and learning best differential evolution with self adaptation for real parameter optimization Proceedings of the 2013 IEEE Congress on Evolutionary Computation 2013 1115 1122
55. R. Kundu, R. Mukherjeea, S. Debchoudhurya, S. Das, P.N. Suganthanc, and T. Vasilakos Improved CMA-ES with memory based directed individual generation for real parameter optimization Proceedings of the 2013 IEEE Congress on Evolutionary Computation 2013 748 755
56. N. Hansen The CMA evolution strategy: a comparing review, towards a new evolutionary computation Adv. Estim. Distrib. Algorithms 2006 75 102
57. C. Igel, T. Suttorp, and N. Hansen A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2006) 2006 453 460
58. S.M. Guo, S.S. Yang, P.H. Hsu, and J.S.H. Tsai Improving differential evolution with successful-parent-selecting framework IEEE Trans. Evol. Comput. PP 99 2014 1 14
59. J. Zhang, and A. Sanderson JADE: adaptive differential evolution with optional external archive IEEE Trans. Evol. Comput. 13 5 2009 945 958
60. A.M. Sutton, M. Lunacek, and L.D. Whitley Differential evolution and non-separability: using selective pressure to focus search Proceedings of the 9th Annu. Conf. GECCO 2007 1428 1435
61. A.K. Qin, V.L. Huang, and P.N. Suganthan Differential evolution algorithm with strategy adaptation for global numerical optimization IEEE Trans. Evol. Comput. 13 2 2009 398 417
62. R. Tanabe, and A. Fukunaga Success-history based parameter adaptation for differential evolution Proceedings of the IEEE Congr. Evol. Comput. 2013 71 78
63. A. Banerjee, U.B. Chitnis, S.L. Jadhav, J.S. Bhawalkar, and S. Chaudhury Hypothesis testing, type I and type II errors Ind. Psychiatry J. 18 2009 127 131 Available from: http://www.industrialpsychiatry.org/text.asp?2009/18/2/127/62274
64. H.R. Tizhoosh Opposition-based learning: a new scheme for machine intelligence Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, vol. I 2005 695 701
65. H. Wang, Z. Wu, Y. Liu, J. Wang, D. Jiang, and L. Chen Space transformation search: a new evolutionary technique Proceedings of Genetic and Evolutionary Computation Conference, GEC Summit 2009 537 544
66. Q. Xu, L. Wang, H. Baomin, and N. Wang Modified opposition-based differential evolution for function optimization J. Comput. Inf. Syst. 7 5 2011 1582 1591
67. M.A. Ahandani, and H. Alavi-Rad Opposition-based learning in the shuffled differential evolution algorithm Soft Comput. 16 8 2012 1303 1337
68. S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama Opposition-based differential evolution IEEE Trans. Evol. Comput. 12 1 2008 64 79
69. M. Ventresca, and H.R. Tizhoosh Improving the convergence of back-propagation by opposite transfer functions Proceedings of the IEEE World Congress and Computational Intelligence 2006 9527 9534
70. H. Wang, Y. Liu, S. Zeng, and C. Li Opposition-based particle swarm algorithm with Cauchy mutation Proceedings of the IEEE Congress on Evolutionary Computation 2007 4750 4756
71. F. Shahzad, A.R. Baig, S. Masood, M. Kamran, and N. Naveed Opposition-based particle swarm optimization with velocity clamping, (OVCPSO) Adv. Intell. Soft Comput. 116 2009 339 348
72. M.G.H. Omran Using opposition-based learning with particle swarm optimization and barebones differential evolution Part. Swarm Optim. 2009 373 384
73. M. Rashid, and A.R. Baig Improved opposition-based PSO for feedforward neural network training Proceedings of the International Conference on Information Science and Applications (ICISA2010) 2010 1 6
74. M. Ergezer, D. Simon, and D. Du Oppositional biogeography-based optimization Proceedings of the IEEE Conference on Systems, Man, and Cybernetics 2009 1035 1040
75. A.R. Malisia, and H.R. Tizhoosh Applying opposition-based ideas to the ant colony system Proceedings of the IEEE Symposium on Foundations of Computational Intelligence 2007 182 189
76. M. Ventresca, and H.R. Tizhoosh Simulated annealing with opposite neighbors Proceedings of the IEEE Symposium on Foundations of Computational Intelligence 2007 186 192
77. H. Wang, Z. Wu, S. Rahnamayan, Y. Liu, and M. Ventresca Enhancing particle swarm optimization using generalized opposition-based learning Inf. Sci. 181 20 2011 4699 4714
78. S.M. Islam, S. Das, S. Ghosh, S. Roy, and P.N. Suganthan An adaptative differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42 2 2012 482 500
79. N. Hansen, and A. Ostermeier Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation Proceedings of the IEEE International Conference on Evolutionary Computation 1996 312 317
80. N. Hansen, S.D. Muller, and P. Koumoutsakos Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES) Evol. Comput. 11 1 2003 1 18
81. A. Auger, and N. Hansen A restart CMA evolution strategy with increasing population size Proceedings of the IEEE Congress on Evolutionary Computation 2005 1769 1776
82. J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Trans. Evol. Comput. 10 3 2006 281 295
83. X. Li, and X. Yao Cooperatively coevolving particle swarms for large scale optimization IEEE Trans. Evol. Comput. 16 2 2012 210 224