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Metaheuristics and Data Clustering

  • Meera Ramadas
  • Ajith Abraham
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 152)

Abstract

In this Chapter, the initial Sects. 2.12.4 give detailed analysis on types of metaheuristics namely Genetic Algorithm, Particle Swarm Optimization, Differential Evolution algorithm and Flower Pollination algorithm. Section 2.5 elaborates the basic data clustering technique and Sect. 2.6 summarizes about the image segmentation. Sections 2.72.9 explains the works related to metaheuristics and their application on data and image clustering respectively.

References

  1. 1.
    Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)CrossRefGoogle Scholar
  2. 2.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43 (1995)Google Scholar
  3. 3.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multi objective optimization. Eng. Optim. 46(9), 1222–1237 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Yang, X.S.: Flower pollination algorithm for global optimization. In: Proceedings of the International conference on unconventional computing and natural computation (pp. 240–249). Springer, Berlin, Heidelberg, (2012)CrossRefGoogle Scholar
  6. 6.
    Pahner, U., Hameyer, K.: Adaptive coupling of differential evolution and multiquadrics approximation for the tuning of the optimization process. IEEE Trans. Magn. 36(4), 1047–1051 (2000)CrossRefGoogle Scholar
  7. 7.
    Abbass, H.A., Sarker, R., Newton, C.: PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 Congress in Evolutionary Computation, vol. 2, pp. 971–978 (2001)Google Scholar
  8. 8.
    Zaharie, D.: Critical values for the control parameters of differential evolution algorithms. In: Proceedings of MENDEL, vol. 2, pp. 62–67 (2002)Google Scholar
  9. 9.
    Madavan, N.K., Bryan, A.B.: Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the Evolutionary Computation on 2002. CEC’02. Proceedings of the 2002 Congress-vol. 02, pp. 1145–1150, IEEE Computer Society (2002)Google Scholar
  10. 10.
    Babu, B.V., Jehan M.M.L.: Differential evolution for multi-objective optimization. In: CEC’03. The 2003 Congress in Evolutionary Computation, vol. 4, pp. 2696–2703. IEEE (2003)Google Scholar
  11. 11.
    Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: CEC’03, The 2003 Congress in Evolutionary Computation, vol. 2, pp. 862–869. IEEE (2003)Google Scholar
  12. 12.
    Fan, H.-Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. J. Glob. Optim. 27(1), 105–129 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Iorio, A.W., Li, X.: Solving rotated multi-objective optimization problems using differential evolution. AI 2004: Advances in Artificial Intelligence, pp. 861–872. Springer, Berlin Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: CEC2004. Congress in Evolutionary Computation, vol. 2, pp. 1382–1389. IEEE (2004)Google Scholar
  15. 15.
    Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791 (2005)Google Scholar
  16. 16.
    Robič, T., Filipič, B.: DEMO: Differential evolution for multiobjective optimization. In: Evolutionary Multi-criterion Optimization, pp. 520–533. Springer, Berlin Heidelberg (2005)Google Scholar
  17. 17.
    Krishnanand, K.N., Ghose, D.: Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and grid systems, 2(3), 209–222 (2006)zbMATHCrossRefGoogle Scholar
  18. 18.
    Chakraborthy, U.K., Das, S., Konar, A.: Differential evolution with local neighbourhood. In: CEC 2006. IEEE Congress in Evolutionary Computation, pp. 2042–2049 (2006)Google Scholar
  19. 19.
    Kim, H.K., Jin, K.C., Kyong, Y.P., David, A.L.: Differential evolution strategy for constrained global optimization and application to practical engineering problems. IEEE Trans. Magn. 43(4), 1565–1568 (2007)CrossRefGoogle Scholar
  20. 20.
    Zhang, J., Chung, H.S.H., Lo, W.L.: Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans. Evol. Comput. 11(3), 326–335 (2007)CrossRefGoogle Scholar
  21. 21.
    Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)CrossRefGoogle Scholar
  22. 22.
    Rahnamayan, S., Tizhoosh, H. R., Salama, M. M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)CrossRefGoogle Scholar
  23. 23.
    Yang, X.S.: Harmony search as a metaheuristic algorithm. Music-Inspired Harmony Search Algorithm, pp. 1–14. Springer, Berlin Heidelberg (2009)Google Scholar
  24. 24.
    Qin, A.K., Vicky, L.H., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRefGoogle Scholar
  25. 25.
    Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)CrossRefGoogle Scholar
  26. 26.
    Jeyakumar, G., Velayutham, C.S.: A comparative performance analysis of differential evolution and dynamic differential evolution variants. In: NaBIC 2009. World Congress on Nature & Biologically Inspired Computing, pp. 463–468. IEEE (2009)Google Scholar
  27. 27.
    Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)Google Scholar
  28. 28.
    Gong, W., Cai, Z., Ling, C.X.: DE/BBO: a hybrid differential evolution with biogeography based optimization for global numerical optimization. Soft. Comput. 15(4), 645–665 (2010)CrossRefGoogle Scholar
  29. 29.
    Takahama, T., Setsuko, S.: Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation. In: IEEE Congress on Evolutionary Computation, pp. 1–9. IEEE (2010)Google Scholar
  30. 30.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar
  31. 31.
    Wang, J., Peng, H., Shi, P.: An optimal image watermarking approach based on a multiobjective genetic algorithm. Inf. Sci. 181, 5501–5514 (2011)CrossRefGoogle Scholar
  32. 32.
    Qu, B.Y., Suganthan, P.N., Liang, J.J.: Differential evolution with neighbourhood mutation for multimodal optimization. IEEE Trans. Evol. Comput. 16(5), 601–614 (2012)CrossRefGoogle Scholar
  33. 33.
    Elsayed, S.M., Sarker, R.A., Essam, D.L.: An improved self-adaptive differential evolution algorithm for optimization problems. IEEE Trans. Ind. Inf. 9(1), pp. 89–99 (2013)CrossRefGoogle Scholar
  34. 34.
    Reed H.M., Nichols J.M., Earls C.J. A modified differential evolution algorithm for damage identification in submerged shell structures. Mech. Syst. Signal Process 39, 396–408 (2013)CrossRefGoogle Scholar
  35. 35.
    Juárez-Abad, J.A., Linares-Flores, J., Guzmán-Ramírez, E., Sira-Ramirez, H.: Generalized proportional integral tracking controller for a single-phase multilevel cascade inverter: An FPGA implementation. IEEE Transactions on Industrial Informatics, 10(1), 256–266 (2014)Google Scholar
  36. 36.
    Yu, W.J., Meie, S., Wei, N.C., Zhi, H.Z., Yue, J.G., Ying, L., Ou, L., Jun, Z.: Differential evolution with two-level parameter adaptation. IEEE Trans. Cybern. 44(7), 1080–1099 (2014)CrossRefGoogle Scholar
  37. 37.
    Cai, Y., Du, J.: Enhanced differential evolution with adaptive direction information. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 305–312 (2014)Google Scholar
  38. 38.
    Cai, Y., Wang, J., Chen, Y., Wang, T., Tian, H., Luo, W.: Adaptive direction information in differential evolution for numerical optimization. Soft Computing, 20(2), 465–494 (2016)CrossRefGoogle Scholar
  39. 39.
    Gong, W., Cai, Z., Liang, D.: Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Trans. Cybern. 45(4), 716–727 (2015)CrossRefGoogle Scholar
  40. 40.
    Guo, S.M., Tsai, J.S.H., Yang, C.C., Hsu, P.H.: A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: Evolutionary Computation, IEEE Congress on (pp. 1003–1010). IEEE (2015)Google Scholar
  41. 41.
    Alam, S., Tawseef, M., Khan, F., Fattah, A.A., Kabir, M.R.: Differential evolution with alternating strategies: a novel algorithm for numeric function optimization. In: Communications on Applied Electronics (CAE), vol. 4, no. 2, pp. 12–16. Foundation of Computer Science FCS, New York, USA. ISSN: 2394–4714 (2016)Google Scholar
  42. 42.
    Guo, Z., Liu, G., Li, D., Wang, S.: Self-adaptive differential evolution with global neighbourhood search. Soft Comput., 1–10 (2016)Google Scholar
  43. 43.
    Qiao, D., Grantham, K.H.P.: A modified differential evolution with heuristic algorithm for nonconvex optimization on sensor network localization. IEEE Trans. Veh. Technol. 65(3), 1676–1689 (2016)CrossRefGoogle Scholar
  44. 44.
    Sakr, W.S., El-Sehiemy, R.A., Azmy A.M.: Adaptive differential evolution algorithm for efficient reactive power management. Appl. Soft Comput. 1(53), 336–351 (2017)CrossRefGoogle Scholar
  45. 45.
    Zaheer, H., Pant, M., Kumar, S., Monakhov, O., Monakhova, E., Deep, K.: A new guiding force strategy for differential evolution. Int. J. Sys. Assur. Eng. Manag. 8(4), 2170–2183 (2017)CrossRefGoogle Scholar
  46. 46.
    Zhang S.X., Zheng S.Y., Zheng L.M.: An efficient multiple variants coordination framework for differential evolution. IEEE Trans. Cybern. 47(9), 2780–2793 (2017)CrossRefGoogle Scholar
  47. 47.
    Qian, S., Ye, Y., Liu, Y., Xu, G.: An improved binary differential evolution algorithm for optimizing PWM control laws of power inverters. Optim. Eng. 19(2), 271–296 (2018)MathSciNetzbMATHCrossRefGoogle Scholar
  48. 48.
    Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Suganthan, P.N.: Ensemble of differential evolution variants. Inf. Sci. 423, 172–186 (2018)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Kaur, G., Singh, D., Kaur, M.: Robust and efficient ‘RGB’ based fractal image compression: flower pollination based optimization. Proc. Int. J. Comput. Appl. 78(10), 11–15 (2013)Google Scholar
  50. 50.
    Kaur, G., Singh, D.: Pollination based optimization for color image segmentation. Int. J. Comput. Eng. Technol. 3(2), (2012)Google Scholar
  51. 51.
    Abdel-Raouf, O., El-Henawy, I., Abdel-Baset, M.: A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles. Int. J. Mod. Educ. Comput. Sci. 6(3), p. 38 (2014)CrossRefGoogle Scholar
  52. 52.
    Abdel-Raouf, O., Abdel-Baset, M.: A new hybrid flower pollination algorithm for solving constrained global optimization problems. Int. J. Appl. Oper. Res.—An Open Access Journal 4(2), 1–13 (2014) Google Scholar
  53. 53.
    Wang, R., Zhou, Y.: Flower pollination algorithm with dimension by dimension improvement. Math. Probl. Eng., 1–9 (2014)Google Scholar
  54. 54.
    Nguyen, T.T., Shieh, C.S., Horng, M.F., Dao, T.K, Ngo, T.G.: Parallelized Flower Pollination algorithm with a communication strategy. In: Knowledge and Systems Engineering (KSE), 2015 Seventh International Conference on IEEE, pp. 103–107 (2015)Google Scholar
  55. 55.
    Zhou, Y., Wang, R., Luo, Q.: Elite opposition-based flower pollination algorithm. Neurocomputing 188, 294–310 (2015)CrossRefGoogle Scholar
  56. 56.
    Paterlini, S., Krink, T.: High performance clustering with differential evolution. In: CEC 2004. Congress on Evolutionary Computation, vol. 2, pp. 2004–2011. IEEE (2004)Google Scholar
  57. 57.
    Zaharie, D.: Density based clustering with crowding differential evolution. In: SYNASC 2005, Seventh International Symposium In Symbolic and Numeric Algorithms for Scientific Computing, p. 8. IEEE (2005)Google Scholar
  58. 58.
    Lu, W.: Unsupervised anomaly detection framework for multiple-connection based network intrusions (Doctoral dissertation). (2005)Google Scholar
  59. 59.
    Martínez-Estudillo, A.C., Hervás-Martínez, C., Martínez-Estudillo, F.J., García-Pedrajas, N.: Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36(3), 534–545 (2005)zbMATHCrossRefGoogle Scholar
  60. 60.
    Abraham, A., Das, S., Konar, A.: Document clustering using differential evolution. In: CEC 2006, IEEE Congress in Evolutionary Computation, pp. 1784–1791 (2006)Google Scholar
  61. 61.
    Alves, V.S., Campello, R.J., Hruschka, E.R.: Towards a fast evolutionary algorithm for clustering. In: Evolutionary Computation, IEEE Congress on (pp. 1776–1783). IEEE (2006)Google Scholar
  62. 62.
    Zhang, Z., Cheng, H., Zhang, S., Chen, W., Fang, Q.: Clustering aggregation based on genetic algorithm for documents clustering. In: Evolutionary computation, IEEE World Congress on Computational Intelligence. IEEE Congress on (pp. 3156–3161). IEEE (2008)Google Scholar
  63. 63.
    Zhang, X., Ding, W., Wang, J., Fan, Z., Deng, G.: Spatial clustering with obstacles constraints using PSO-DV and K-medoids. In: ISKE 2008. 3rd International Conference in Intelligent System and Knowledge Engineering, vol. 1, pp. 246–251. IEEE (2008)Google Scholar
  64. 64.
    Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 38(1), 218–237 (2008)CrossRefGoogle Scholar
  65. 65.
    Indrajit, S., Ujjwal, M., Nilan, J.: Differential fuzzy clustering for categorical data. In: ICM2CS 2009. Proceeding of International Conference in Methods and Models in Computer Science, pp. 1–6. IEEE (2009)Google Scholar
  66. 66.
    Zheng, Z., Gong, M., Ma, L., Jiao, J., Wu, Q.: Unsupervised evolutionary clustering algorithm for mixed type data. In: 2010 IEEE Congress in Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)Google Scholar
  67. 67.
    Pawlak, Z.: Rough set theory and its applications to data analysis. Cybern. Syst. 29(7), 661–688 (1998)zbMATHCrossRefGoogle Scholar
  68. 68.
    Maulik, U., Saha, I.: Automatic fuzzy clustering using modified differential evolution for image classification. IEEE Trans. Geosci. Remote Sens. 48(9), 3503–3510 (2010)CrossRefGoogle Scholar
  69. 69.
    Maulik, U., Saha, I.: Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recogn. 42, 2135–2149 (2009)zbMATHCrossRefGoogle Scholar
  70. 70.
    Alguliev, R.M., Aliguliyev, R.M., Hajirahimova, M.S., Mehdiyev, C.A.: MCMR: Maximum coverage and minimum redundant text summarization model. Expert systems with applications, 38(12), 14514–14522 (2011)CrossRefGoogle Scholar
  71. 71.
    Pham, D.T., Suarez-Alvarez, M.M., Prostov, Y.I.: Random search with k-prototypes algorithm for clustering mixed datasets. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 467(2132), 2387–2403 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  72. 72.
    Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)MathSciNetCrossRefGoogle Scholar
  73. 73.
    Suarez-Alvarez, M.M., Pham, D.T., Prostov, M.Y., Prostov, Y.I.: Statistical approach to normalization of feature vectors and clustering of mixed datasets. Proc. R. Soc. A (p. rspa20110704) (2012)Google Scholar
  74. 74.
    Voges, K., Pope, N.: Generating compact rough cluster descriptions using an evolutionary algorithm. In: Genetic and Evolutionary Computation Conference (pp. 1332–1333). Springer, Berlin, Heidelberg (2004)CrossRefGoogle Scholar
  75. 75.
    He, Y., Jian W., Liang-xi Q., Lin M., Yan-feng S., Wen-fei, W.: A HK clustering algorithm based on ensemble learning. In: IET International Conference in Smart and Sustainable City 2013 (ICSSC 2013), pp. 300–305. IET (2013)Google Scholar
  76. 76.
    Saha, S., Bandyopadhyay, S.: A symmetry based multiobjective clustering technique for automatic evolution of clusters. Pattern recognition, 43(3), 738–751 (2010)zbMATHCrossRefGoogle Scholar
  77. 77.
    Singh, V., Saha, S.: Modified differential evolution based 0/1 clustering for classification of data points: using modified new point symmetry based distance and dynamically controlled parameters. In: 2014 International Conference in Contemporary Computing and Informatics (IC3I), pp. 1182–1187. IEEE (2014)Google Scholar
  78. 78.
    Ameryan, M., Totonchi M.R.A., Mahdavi S.J.S.: Clustering based on Cuckoo optimization algorithm. In: 2014 Iranian Conference in Intelligent Systems (ICIS), pp. 1–6. IEEE (2014)Google Scholar
  79. 79.
    Thein, H.T.T., Khin, M.M.T.: Evaluation of differential evolution and K-means algorithms on medical diagnosis. In: 5th National Symposium in Information Technology: Towards New Smart World (NSITNSW), pp. 1–4. IEEE (2015)Google Scholar
  80. 80.
    Ozturk, C., Hancer, E., Karaboga, D.: Dynamic clustering with improved binary artificial bee colony algorithm. Appl. Soft Comput. 28, 69–80 (2015)CrossRefGoogle Scholar
  81. 81.
    Mukherjee, R., Debchoudhury, S., Das, S.: Modified differential evolution with locality induced genetic operators for dynamic optimization. Eur. J. Oper. Res. 253(2), 337–355 (2016)zbMATHCrossRefGoogle Scholar
  82. 82.
    Wu, G., Mallipeddi, R., Suganthan, P.N., Wang, R., Chen, H.: Differential evolution with multi-population based ensemble of mutation strategies. Information Sciences, 329, 329–345 (2016)CrossRefGoogle Scholar
  83. 83.
    Cheng, M.Y., Tran, D.H., Hoang, N.D.: Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects. J. Civil Eng. Manag. 23(1), 113–124 (2017)CrossRefGoogle Scholar
  84. 84.
    Hancer, E., Karaboga, D.: A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm Evol. Comput. 32, 49–67 (2017)CrossRefGoogle Scholar
  85. 85.
    Saha, S., Das, R.: Exploring differential evolution and particle swarm optimization to develop some symmetry-based automatic clustering techniques: application to gene clustering. Neural Comput. Appl. 30(3), 735–757 (2018)CrossRefGoogle Scholar
  86. 86.
    Pal, S.K., Bhandari, D., Kundu, M.K.: Genetic algorithms for optimal image enhancement. Pattern Recogn. Lett. 15, 261–271 (1994)zbMATHCrossRefGoogle Scholar
  87. 87.
    Shyu, M., Leou, J.: A genetic algorithm approach to color image enhancement. Pattern Recogn. 31(7), 871–880 (1998)CrossRefGoogle Scholar
  88. 88.
    Tao, W., Tian, J., Liu, J.: Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm. Pattern Recogn. Lett. 24, 3069–3078 (2003)CrossRefGoogle Scholar
  89. 89.
    Magoulas, G.D., Plagianakos, V.P., Vrahatis, M.N.: Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Appl. Soft Comput. 4, 369–379 (2004)CrossRefGoogle Scholar
  90. 90.
    Roula, M.A., Ahmed, B., Fatih, K.: An evolutionary snake algorithm for the segmentation of nuclei in histopathological images. In: ICIP’04. 2004 International Conference in Image Processing, vol. 1, pp. 127–130. IEEE (2004)Google Scholar
  91. 91.
    Zahara, E., Fan, S.S., Tsai, D.: Optimal multi-thresholding using a hybrid optimization approach. Pattern Recogn. Lett. 26, 1082–1095 (2005)CrossRefGoogle Scholar
  92. 92.
    Shih, F.Y., Wu, T.: Enhancement of image watermark retrieval based on genetic algorithms. J. Vis. Commun. Image Represent. 16, 115–133 (2005)CrossRefGoogle Scholar
  93. 93.
    Omran, M.G.H., Andries, P.E., Ayed, S.: Differential evolution methods for unsupervised image classification. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 966–973 (2005)Google Scholar
  94. 94.
    Feng, D., Wenkang, S., Liangzhou, C., Yong, D., Zhenfu, Z.: Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO). Pattern Recogn. Lett. 26, 597–603 (2005)CrossRefGoogle Scholar
  95. 95.
    Rahnamayan, S., Hamid R.T., Magdy, M.A.S.: Image thresholding using differential evolution. In: IPCV, pp. 244–249 (2006)Google Scholar
  96. 96.
    Dehmeshki, J., Ye, X., Lin, X.Y., Valdivieso, M., Amin, H.: Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med. Imaging Graph. 31, 408–417 (2007)CrossRefGoogle Scholar
  97. 97.
    Aslantas, V., Tunckanat, M.: Differential evolution algorithm for segmentation of wound images. In: WISP 2007. IEEE International Symposium on Intelligent Signal Processing, pp. 1–5. IEEE (2007)Google Scholar
  98. 98.
    Jiang, J., Yao, B., Wason, A.M.: A genetic algorithm design for micro calcification detection and classification in digital mammograms. Comput. Med. Imaging Graph. 31, 49–61 (2007)CrossRefGoogle Scholar
  99. 99.
    Fan, S.S., Lin, Y.: A multi-level thresholding approach using a hybrid optimal estimation algorithm. Pattern Recogn. Lett. 28, 662–669 (2007)CrossRefGoogle Scholar
  100. 100.
    Yin, P.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184, 503–513 (2007)MathSciNetzbMATHGoogle Scholar
  101. 101.
    Li, L., Li, D.: Fuzzy entropy image segmentation based on particle swarm optimization. Prog. Nat. Sci. 18, 1167–1171 (2008)CrossRefGoogle Scholar
  102. 102.
    Maitra, M., Chatterjee, A.: A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst. Appl. 34, 1341–1350 (2008)CrossRefGoogle Scholar
  103. 103.
    Falco, I.D., Cioppa, A.D., Maisto, D., Tarantino, E.: Differential evolution as a viable tool for satellite image registration. Appl. Soft Comput. 8, 1453–1462 (2008)CrossRefGoogle Scholar
  104. 104.
    Hammouche, K., Diaf, M., Siarry, P.: A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput. Vis. Image Underst. 109, 163–175 (2008)CrossRefGoogle Scholar
  105. 105.
    Basturk, A., Gunay, E.: Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm. Expert Syst. Appl. 36, 2645–2650 (2009)CrossRefGoogle Scholar
  106. 106.
    Hasan, H., Haron, H., Hashim, S.Z.: Freeman chain code extraction using differential evolution (DE) and particle swarm optimization (PSO). In: SOCPAR’09. International Conference in Soft Computing and Pattern Recognition, pp. 77–81. IEEE (2009)Google Scholar
  107. 107.
    Coelho, L.S., Sauer, J.G., Rudek, M.: Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos, Solitons Fractals 42, 522–529 (2009)CrossRefGoogle Scholar
  108. 108.
    Das, S., Konar, A.: Automatic image pixel clustering with an improved differential evolution. Appl. Soft Comput. 9, 226–236 (2009)CrossRefGoogle Scholar
  109. 109.
    Forouzanfar, M., Forghani, N., Teshnehlab, M.: Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Eng. Appl. Artif. Intell. 23, 160–168 (2010)CrossRefGoogle Scholar
  110. 110.
    Zhang, C., Wang, X., Duanmu, C.: Adaptive typhoon cloud image enhancement using genetic algorithm and non-linear gain operation in undecimated wavelet domain. Eng. Appl. Artif. Intell. 23, 61–73 (2010)CrossRefGoogle Scholar
  111. 111.
    Hashemi, S., Kiani, S., Noroozi, N., Moghaddam, M.E.: An image contrast enhancement method based on genetic algorithm. Pattern Recogn. Lett. 31, 1816–1824 (2010)CrossRefGoogle Scholar
  112. 112.
    Korürek, M., Yüksel, A., Iscan, Z., Dokur, Z., Ölmez, T.: Retrospective correction of near field effect of X-ray source in radiographic images by using genetic algorithms. Expert Syst. Appl. 37, 1946–1954 (2010)CrossRefGoogle Scholar
  113. 113.
    Perez, A.C., Aravena, C.M., Vallejos, J.I., Estevez, P.A., Held, C.M.: Face and iris localization using templates designed by particle swarm optimization. Pattern Recogn. Lett. 31, 857–868 (2010)CrossRefGoogle Scholar
  114. 114.
    Papa, J.P., Fonseca, L.M.G., de Carvalho, L.A.S.: Projections on to convex sets through particle swarm optimization and its application for remote sensing image restoration. Pattern Recogn. Lett. 31, 1876–1886 (2010)CrossRefGoogle Scholar
  115. 115.
    Das, S., Sil, S.: Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inf. Sci. 180, 1237–1256 (2010)MathSciNetCrossRefGoogle Scholar
  116. 116.
    Cuevas, E., Zaldivar, D., Pérez-Cisneros, M.: A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst. Appl. 37, 5265–5271 (2010)CrossRefGoogle Scholar
  117. 117.
    Azarbad, M., Ebrahimzadeh, A., Babajani-Feremi, A.: Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm. In: 2010 17th Iranian Conference in Biomedical Engineering (ICBME), pp. 1–6. IEEE (2010)Google Scholar
  118. 118.
    Aslantas, V., Kurban, R.: Fusion of multifocus images using differential evolution algorithm. Expert Syst. Appl. 37, 8861–8870 (2010)CrossRefGoogle Scholar
  119. 119.
    Abuhaiba, I.S.I., Hassan, M.A.S.: Image encryption using differential evolution approach in frequency domain. Signal Image Process. 2, 51–69 (2011)Google Scholar
  120. 120.
    Kumar, S., Pant M., Ray A.K.: Differential evolution embedded Otsu’s method for optimized image thresholding. In: 2011 World Congress on Information and Communication Technologies (WICT), pp. 325–329. IEEE (2011)Google Scholar
  121. 121.
    Mesejo, P., Ugolotti, R., Di Cunto, F., Giacobini, M., Cagnoni, S.: Automatic hippocampus localization in histological images using Differential Evolution-based deformable models. Pattern Recogn. Lett., 34(3), 299–307 (2013)CrossRefGoogle Scholar
  122. 122.
    Tang, K., Yuan, X., Sun, T., Yang, J., Gao, S.: An improved scheme for minimum cross entropy threshold selection based on genetic algorithm. Knowl.-Based Syst. 24, 1131–1138 (2011)CrossRefGoogle Scholar
  123. 123.
    Mukhopadhyay, A., Maulik, U.: A multi-objective approach to MR brain image segmentation. Appl. Soft Comput. 11, 872–880 (2011)CrossRefGoogle Scholar
  124. 124.
    Kwedlo, W.: A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn. Lett. 32, 1613–1621 (2011)CrossRefGoogle Scholar
  125. 125.
    Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst. Appl. 38, 4998–5004 (2011)CrossRefGoogle Scholar
  126. 126.
    Zhang, Y., Huang, D., Ji, M., Xie, F.: Image segmentation using PSO and PCM with Mahalanobis distance. Expert Syst. Appl. 38, 9036–9040 (2011)CrossRefGoogle Scholar
  127. 127.
    Wang, L., Cao, J., Han, C.: Multidimensional particle swarm optimization-based unsupervised planar segmentation algorithm of unorganized point clouds. Pattern Recogn. 45, 4034–4043 (2012)CrossRefGoogle Scholar
  128. 128.
    Masra, S.M.W., Pang, P.K., Muhammad, M.S., Kipli, K.: Application of particle swarm optimization in histogram equalization for image enhancement. In: IEEE Colloquium on Humanities, Science & Engineering Research, pp. 294–299 (2012)Google Scholar
  129. 129.
    Chen, H., Leou, J.: Saliency-directed color image interpolation using artificial neural network and particle swarm optimization. J. Vis. Commun. Image Represent. 23, 343–358 (2012)CrossRefGoogle Scholar
  130. 130.
    Pavan, K.K., Srinivas, V.S., SriKrishna, A., Reddy, B.E.: Automatic tissue segmentation in medical images using differential evolution. J. Appl. Sci. 12(6), 587–592 (2012)CrossRefGoogle Scholar
  131. 131.
    Santamaría, J., Damas, S., García-Torres, J.M., Cordón, O.: Self-adaptive evolutionary image registration using differential evolution and artificial immune systems. Pattern Recogn. Lett. 33, 2065–2070 (2012)CrossRefGoogle Scholar
  132. 132.
    Nakib, A., Daachi, B., Siarry, P.: Hybrid Differential evolution using low discrepancy sequences for image segmentation. In: IEEE 26th International Parallel and Distributed Processing Symposium Workshops & Ph.D. Forum (IPDPSW), pp. 634–640 (2012)Google Scholar
  133. 133.
    Vahedi, E., Zoroofi, R.A., Shiva, M.: Toward a new wavelet-based watermarking approach for color images using bio-inspired optimization principles. Digit. Signal Process. 22, 153–162 (2012)CrossRefGoogle Scholar
  134. 134.
    Li, Z., Xiuwan, C., Peng, L., Yuan, T.: Water area segmentation of the Yangcheng Lake with SAR data based on improved 2D maximum entropy and genetic algorithm. In: 2012 Second International Workshop in Earth Observation and Remote Sensing Applications (EORSA), pp. 263–267. IEEE (2012)Google Scholar
  135. 135.
    Wu, W., Lina, S., Moon, W.K.: Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Comput. Med. Imaging Graph. 36, 627–633 (2012)CrossRefGoogle Scholar
  136. 136.
    Lee, C., Leou, J., Hsiao, H.: Saliency-directed color image segmentation using modified particle swarm optimization. Sig. Process. 92, 1–18 (2012)CrossRefGoogle Scholar
  137. 137.
    Tsai, H., Chang, B., Lin, X.: Using decision tree, particle swarm optimization, and support vector regression to design a median-type filter with a 2-level impulse detector for image enhancement. Inf. Sci. 195, 103–123 (2012)CrossRefGoogle Scholar
  138. 138.
    Dong, N., Wu, C., Ip, W., Chen, Z., Chan, C., Yung, K.: An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection. Comput. Math. Appl. 64, 1886–1902 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  139. 139.
    Sumer, E., Turker, M.: An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images. Comput. Environ. Urban Syst. 39, 48–62 (2013)CrossRefGoogle Scholar
  140. 140.
    Vellasques, E., Sabourin, R., Granger, E.: Fast intelligent watermarking of heterogeneous image streams through mixture modeling of PSO populations. Appl. Soft Comput. 13, 3130–3148 (2013)CrossRefGoogle Scholar
  141. 141.
    Benaichouche, A.N., Oulhadj, H., Siarry, P.: Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digital Signal Processing, 23(5), 1390–1400 (2013)MathSciNetCrossRefGoogle Scholar
  142. 142.
    Gao, H., Kwong, S., Yang, J., Cao, J.: Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation. Inf. Sci. 250, 82–112 (2013)MathSciNetCrossRefGoogle Scholar
  143. 143.
    Osuna-Enciso, V., Cuevas, E., Sossa, H.: A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Syst. Appl. 40, 1213–1219 (2013)CrossRefGoogle Scholar
  144. 144.
    Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13, 3066–3091 (2013)CrossRefGoogle Scholar
  145. 145.
    Kwok, N.M., Shi, H.Y., Ha, Q.P., Fang, G., Chen, S.Y., Jia, X.: Simultaneous image color correction and enhancement using particle swarm optimization. Eng. Appl. Artif. Intell. 26, 2356–2371 (2013)CrossRefGoogle Scholar
  146. 146.
    Hoseini, P., Shayesteh, M.G.: Efficient contrast enhancement of images using hybrid ant colony optimization, genetic algorithm, and simulated annealing. Digit. Signal Process. 23, 879–893 (2013)MathSciNetCrossRefGoogle Scholar
  147. 147.
    Galbally, J., Ross, A., Gomez-Barrero, M., Fierrez, J., Ortega-Garcia, Javier: Iris image reconstruction from binary templates: an efficient probabilistic approach based on genetic algorithms. Comput. Vis. Image Underst. 117, 1512–1525 (2013)CrossRefGoogle Scholar
  148. 148.
    Sarkar, S., Das, S.: Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—a differential evolution approach. IEEE Trans. Image Process. 22(12), 4788–4797 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  149. 149.
    Ugolotti, R., Nashed, Y.S.G., Mesejo, P., Ivekovi, S., Mussia, L., Cagnoni, S.: Particle swarm optimization and differential evolution for model-based object detection. Appl. Soft Comput. 13, 3092–3105 (2013)CrossRefGoogle Scholar
  150. 150.
    Novo, J., Santos, J., Penedo, M.G.: Multi-objective differential evolution in the optimization of topological active models. Appl. Soft Comput. 13, 3167–3177 (2013)CrossRefGoogle Scholar
  151. 151.
    Paul, S., Bitan, B.: A novel approach for image compression based on multi-level image thresholding using Shannon Entropy and Differential Evolution. In: Students’ Technology Symposium (TechSym), pp. 56–61. IEEE (2014)Google Scholar
  152. 152.
    Ali, M., Ahn, C.W., Pant, M.: A robust image watermarking technique using SVD and differential evolution in DCT domain. Optik 125, 428–434 (2014)CrossRefGoogle Scholar
  153. 153.
    Ali, M., Ahn, C.W., Pant, M.: Multilevel image thresholding by synergetic differential evolution. Appl. Soft Comput. 17, 1–11 (2014)CrossRefGoogle Scholar
  154. 154.
    Shanmugavadivu, P., Balasubramanian, K.: Particle swarm optimized multiobjective histogram equalization for image enhancement. Opt. Laser Technol. 57, 243–251 (2014)CrossRefGoogle Scholar
  155. 155.
    Lei, B., Tan, E., Chen, S., Ni, D., Wanga, T., Lei, H.: Reversible watermarking scheme for medical image based on differential evolution. Expert Syst. Appl. 41, 3178–3188 (2014)CrossRefGoogle Scholar
  156. 156.
    Ochoa-Montiel, R., Sánchez-López, C., González-Bernal, J.A.: Thresholding of biological images by using evolutionary algorithms. In: 2015 Latin America Congress on Computational Intelligence (LA-CCI), pp. 1–6 (2015)Google Scholar
  157. 157.
    Allaoui, A.E., Nasri, M.B.: Threshold optimization by genetic algorithm for segmentation of medical images by region growing. International Journal of Emerging Trends and Technology in Computer Science (IJETTCS), 1(2), 161–166 (2012)Google Scholar
  158. 158.
    Samanta, S.O., Choudhury, A.L., Dey, N., Ashour, A.S., Balas V.E.: Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding. Quant. Inspired Comput. Intell., 285–326 (2017)Google Scholar
  159. 159.
    Zhong Y., Ma, A., Soon Ong, Y., Zhu, Z., Zhang, L.: Computational intelligence in optical remote sensing image processing. Appl. Soft Comput. (2017)Google Scholar
  160. 160.
    Kumar, S., Pant, M., Kumar, M., Dutt, A.: Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms. Int. J. Mach. Learn. Cybern. 9(1), 163–183 (2018)CrossRefGoogle Scholar
  161. 161.
    Ali, M., Ahn, C.W.: An optimal image watermarking approach through cuckoo search algorithm in wavelet domain. Int. J. Syst. Assur. Eng. Manag. 9(3), 602–611 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information TechnologyUniversity College of BahrainManamaBahrain
  2. 2.Scientific Network for Innovation and Research ExcellenceMachine Intelligence Research Labs (MIR Labs)AuburnUSA

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