Sequentially Modified Gravitational Search Algorithm for Image Enhancement

Yıl 2020, Cilt: 8 Sayı: 4, 2266 - 2288, 29.10.2020

Ferzan Katırcıoğlu , Uğur Güvenç

https://doi.org/10.29130/dubited.710153

Öz

Gravitational Search Algorithm (GSA) is based on the acceleration trend feature of objects with a mass towards each other and includes many interdependent parameters. The gravitational constant among these parameters influences the speeds and positions of the agents, meaning that the search capability depends on the largescale gravitational constant. The proposed new algorithm, which was obtained with the use of two operators at different times of the call and sequentially doing works, was named as Sequentially Modified ‎ Gravitational Search Algorithm (SMGSA). SMGSA is applied to 10 basic and 6 composite benchmark functions. Each function is run 30 times and the best, mean and median values are obtained. The achieved results are compared with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GSA among the heuristic optimization algorithms. Between GSA and the operator for each function convergence speed, standard deviation and graphical comparisons are included. Beside this, by using the Wilcoxon signed rank test, the comparison of the averages of the data as two dependent groups of GSA and the new operators is performed. It is seen that the obtained results provided better results than the other methods. Additionally, in this study, SMGSA was applied to the transformation function among image enhancement techniques which are engineering applications. The success of this method has been increased by optimizing the parameters of the transformation function used. Effective improvement has been achieved in terms of both visual and information quality.

Anahtar Kelimeler

Gravitational Search Algorithm, Optimization, Image Enhancement

Görüntü İyileştirme için Sıralı Modifiyeli Yerçekimi Arama Algoritması

Öz

Yerçekimi Arama Algoritması (GSA), kütlesi birbirine yakın olan nesnelerin hızlanma eğilimi özelliğini temel almakta olup, birbirine bağlı birçok parametre içermektedir. Bu parametreler arasındaki yerçekimi sabiti ajanların hızlarını ve konumlarını etkiler, yani arama kabiliyeti büyük ölçekli yerçekimi sabitine bağlıdır. Bu çalışmada, farklı zamanlarda iki operatörün kullanılması ve sırayla çalışmalarını kapsayan yeni algoritma önerilmiştir ve Sıralı Değiştirilmiş Yerçekimi Arama Algoritması (SMGSA) olarak adlandırılmıştır. SMGSA 10 temel ve 6 kompozit kıyaslama fonksiyonuna uygulanmaktadır. Her fonksiyon 30 kez çalıştırılır ve en iyi, ortalama ve medyan değerler elde edilmektedir. Elde edilen sonuçlar sezgisel optimizasyon algoritmaları arasında Genetik Algoritma (GA), Parçacık Sürüsü Optimizasyonu (PSO) ve GSA ile karşılaştırılmıştır. GSA ile operatör arasında her fonksiyon yakınsama hızı için standart sapma ve grafik karşılaştırmalar bulunmuştur. Bunun yanı sıra, Wilcoxon sıralama testi kullanılarak, verilerin ortalamalarının iki bağımlı GSA grubu ve yeni operatörler olarak karşılaştırılması gerçekleştirilmiştir. Ayrıca bu çalışmada, mühendislik uygulamalarından görüntü iyileştirme teknikleri arasında yer alan dönüşüm fonksiyonuna SMGSA uygulanmıştır. Bu yöntemin başarısı, kullanılan dönüştürme fonksiyonunun parametreleri optimize edilerek arttırılmıştır. Hem görsel hem de bilgi kalitesi açısından etkili bir gelişme sağlanmıştır.

Anahtar Kelimeler

Yerçekimi Arama Algoritması, Optimizasyon

Kaynakça

• [1] C. R. Reeves, Modern heuristic techniques for combinatorial problems, John Wiley & Sons, Inc, 1993.
• [2] T. Cura, Modern heuristic techniquies and applications, Papatya, Istanbul, 2008.
• [3] S. Salhi, Heuristic search methods, Mahwah, NJ: Erlbaum, 1998.
• [4] A. R. Bhowmik, A. K. Chakraborty, “Solution of optimal power flow using nondominated sorting multi objective gravitational search algorithm,” Electrical Power and Energy Systems, vol. 62, pp. 323-334, 2014.
• [5] C. Li, H. Li, and P. Kou, “Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system,” Neurocomputing, vol. 124, pp. 139-148, 2013.
• [6] J. Vijaya Kumar, D. M. Vinod Kumar, and K. Edukondalu, “Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market,” Applied Soft Computing, vol. 13, pp. 2445-2455, 2012.
• [7] H. Askari, S. H. Zahiri, “Intelligent gravitational search algorithm for optimum design of fuzzy classifier,” 2nd Intermational eConference on Computer and Knowledge Engineering,Mashhad, Iran, 2012, pp. 98-104.
• [8] Y. Sun, Z. Tang, J. Lu, P. Du, “Optimal Multilevel Thresholding using Improved Gravitational Search Algorithm for Image Segmentation,” In Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on IEEE, Shenyang, China, 2013, pp. 1487-1490.
• [9] A. Sombra, F. Valdez, P. Melin, “Castillo O. A new gravitational search algorithm using fuzzy logic to parameter adaptation,” InEvolutionary Computation (CEC) 2013 IEEE Congress on IEEE,Cancun, Mexico, 2013, pp. 1068-1074.
• [10] F. Saeidi-Khabisi, E. Rashedi, “Fuzzy Gravitational Search Algorithm,” 2nd International eConference on Computer and Knowledge Engineering, Mashhad, Iran, 2012, pp. 156-160.
• [11] G. Sun, A. Zhang, Y. Yao, Z. Wang, “A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding,” Applied Soft Computing, vol. 46, pp. 703-730, 2016.
• [12] C. Li, L. Chang, Z. Huang, Y. Liu, N. Zhang, “Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm,” Engineering Applications of Artificial Intelligence, vol. 50, pp. 177-191, 2016.
• [13] G. Sun, P. Ma, J. Ren, A. Zhang, X. Jia, “A stability constrained adaptive alpha for gravitational search algorithm,” Knowledge-Based Systems, vol. 139, pp. 200-213, 2018.
• [14] U. Güvenç, F. Katırcıoğlu, “Escape velocity: a new operator for gravitational search algorithm,” Neural Computing and Applications, vol. 31, no. 1, pp. 27-42, 2019.
• [15] K. Kang, C. Bae, H. W. F. Yeung, Y. Y. Chung, “A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization,” Applied Soft Computing, vol. 66, pp. 319-329, 2018.
• [16] R. J. Schalkoff, “Digital image processing and computer vision,” New York: Wiley, 1989.
• [17] J. C. Russ, F. B. Neal, “The image processing handbook,” 7th ed., CRC press, 2017.
• [18] Y. T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE ‎transactions on Consumer Electronics, vol. 43, no. 1, pp. 1-8, 1997.
• [19] S. D. Chen, A. R. Ramli, “Contrast enhancement using recursive mean-separate histogram ‎equalization for scalable brightness preservation,” IEEE Transactions on consumer Electronics, vol. 49, no. 4, pp. ‎‎1301-1309, 2003. ‎
• [20] K. S. Sim, C. P. Tso, Y. Y. Tan, “Recursive sub-image histogram equalization applied to gray ‎scale images,” Pattern Recognition Letters, vol. 28, no. 10, pp. 1209-1221, 2007.
• [21] G. Tanaka, N. Suetake, E. Uchino, “Image enhancement based on multiple ‎parametric sigmoid functions,” In Intelligent Signal Processing and Communication Systems, 2007 ‎ISPACS, 2007, pp. 108-111.
• [22] P. Kannan, S. Deepa, R. Ramakrishnan, “Contrast enhancement of sports images ‎using modified sigmoid mapping function,” In Communication Control and Computing Technologies ‎‎(ICCCCT), 2010, pp. 651-656.
• [23] H. K. Verma, S. Pal, “Modified Sigmoid Function Based Gray Scale Image Contrast ‎Enhancement Using Particle Swarm Optimization,” Journal of The Institution of Engineers (India): Series ‎B, vol. 97, no. 2, pp. 243-251, 2016. ‎
• [24] C. Munteanu, A. Rosa, “Towards automatic image enhancement using genetic algorithms,” In ‎Evolutionary Computation, Proceedings of the 2000 Congress on 2, 2000, pp. 1535-1542. ‎
• [25] A. Gorai, A. Ghosh, “Gray-level image enhancement by particle swarm ‎optimization,” In Nature & Biologically Inspired Computing, pp. ‎‎72-77, 2009. ‎
• [26] W. Zhao, “Adaptive image enhancement based on gravitational search algorithm,” Procedia ‎Engineering, vol. 15, pp. 3288-3292, 2011.
• [27] S. Agrawal, R. Panda, “An efficient algorithm for gray level image enhancement ‎using cuckoo search,” In International Conference on Swarm, Evolutionary, and Memetic Computing, ‎‎pp. 82-89, 2012.‎
• [28] P. P. Sarangi, B. S. P. Mishra, B. Majhi, S. Dehuri, “Gray-level image enhancement ‎using differential evolution optimization algorithm,” In Signal Processing and Integrated Networks ‎‎(SPIN), 2014 International Conference, 2014, pp. 95-100.
• [29] K. Murali, T. Jayabarathi, “Automated image enhancement using Grey-wolf optimizer algorithm,” J Multidiscip Sci Technol, vol. 7, pp. 77-84, 2016.
• [30] A. M. Nickfarjam, H. Ebrahimpour-Komleh, “Multi-resolution gray-level image enhancement using particle swarm optimization,” Applied Intelligence, vol. 47, no. 4, pp. 1132-1143, 2017.
• [31] K. G. Dhal, S. Ray, A. Das, S. Das, “A survey on nature-inspired optimization algorithms and their application in image enhancement domain,” Archives of Computational Methods in Engineering, vol. 26, no. 5, pp. 1607-1638, 2019.
• [32] H. Singh, A. Kumar, L. K. Balyan, G. K. Singh, “A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement,” Computers & Electrical Engineering, vol. 75, pp. 245-261, 2019.
• [33] P. Kandhway, A. K. Bhandari, A. Singh, “A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization,” Biomedical Signal Processing and Control, vol. 56, pp. 101677, 2020.
• [34] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, “GSA: a gravitational search algorithm,” Information sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
• [35] S. Sarafrazi, H. Nezamabadi-Pour, S. Saryazdi, “Disruption: a new operator in gravitational search algorithm,” Scientia Iranica, vol. 18, no. 3, pp. 539-548, 2011.
• [36] X. Han, X. A. Chang, “Chaotic digital secure communication based on a modified gravitational search algorithm filter,” Information Sciences, vol. 208, pp. 14-27, 2012.
• [37] S. Mirjalili, S. Z. M. Hashim, “A new hybrid PSOGSA algorithm for function optimization,” In Computer and information application (ICCIA), 2010 international conference on IEEE,Tianjin, China, pp. 374-377, 2010.
• [38] B. Gu, F. Pan, “Modified gravitational search algorithm with particle memory ability and its application,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 11, pp. 4531-4544, 2013.
• [39] F. Katircioglu, U. Güvenc, “Sarsıntı Operatörü: Yerçekimi Arama Algoritması İçin Yeni Bir Operatör,”. Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu ASYU, 2016, pp. 132-137.
• [40] F. Katircioglu, “Improving a new operators for Gravitation Search Algorithm,” Doctoral Thesis, Graduate School of Natural and Applied Sciences, Department of Electrical-Electronic and Computer Engineer, Duzce University, Duzce, Turkey, 2016.
• [41] S. Mirjalili, A. H. Gandomi, “Chaotic gravitational constants for the gravitational search algorithm,” Applied Soft Computing, vol. 53, pp. 407-419, 2017.
• [42] G. Wang, S. He, “A quantitative study on detection and estimation of weak signals by using chaotic Duffing oscillators,” Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions, vol. 50, no. 7, pp. 945-953, 2003.
• [43] E. N. Lorenz, “Deterministic nonperiodic flow,” Journal of the atmospheric sciences, vol. 20, no. 2, pp. 130-141, 1963.
• [44] E. Ott, C. Grebogi, J. A. Yorke, “Controlling chaos,” Physical review letters, vol. 64, no. 11, pp. 1196, 1990.
• [45] T. L. Liao, S. H. Tsai, “Adaptive synchronization of chaotic systems and its application to secure communications,” Chaos, Solitons & Fractals, vol. 11, no. 9, pp. 1387-1396, 2000.
• [46] S. Haykin, B. Li, “Detection of signals in chaos,” Proceedings of the IEEE, vol. 83, no. 1, pp. 95-122, 1995.
• [47] L. Yang, T. L. Chen, “Application of chaos in genetic algorithms. Commun Theor Phys, vol. 38, no. 1, pp. 168-172, 2002.
• [48] G. Zhenyu, L. Jia, X. Gao, J. Liu, F. Wu, “Self-adaptive chaos differential evolution,” Advances in natural computation, vol. 4221, pp. 972-975, 2006.
• [49] D. Simon, “Biogeography-based optimization,” IEEE Trans Evol Comput, vol. 12, pp. 702-713, 2008.
• [50] D. Du, D. Simon, M. Ergezer, “Biogeography-based optimization combined with evolutionary strategy and immigration refusal,” IEEE international conference on systems, 2009, pp. 997-1002.
• [51] U. Güvenc, F. Katircioğlu, “En iyi ajana özel davranış: Geliştirilmiş yerçekimi arama algoritması,” Ecjse Journal of Science and Engineering, vol. 3, no. 1, pp. 143-153, 2016.
• [52] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, S. Tiwari, “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization.” KanGAL report, Singapore, 2005.
• [53] R. L. Haupt, S. E. Haupt, Practical genetic algorithms. John Wiley & Sons, Pennsylvania, 2004.
• [54] H. C. Tsai, Y. Y. Tyan, Y. W. Wu, Y. H. Lin, “Gravitational particle swarm,” Applied Mathematics and Computation, vol. 219, no. 17, pp. 9106-9117, 2013.
• [55] X. Han, L. Quan, X. Xiong, B. Wu, “Diversity enhanced and local search accelerated gravitational search algorithm for data fitting with B-splines,” Engineering with Computers, vol. 31, no. 2, pp. 215-236, 2015.
• [56] S. Garcia, “A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the cec'2005 special on real parameter optimization,” Journal of Heuristics, vol. 15, no. 6, pp. 617-644, 2009.
• [57] F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics, vol. 1, no. 6, pp. 80-83, 1945.
• [58] A. Zhang, “A hybrid genetic algorithm and gravitational search algorithm for global optimization,” Neural Network World, vol. 25, no. 3, pp. 53-73, 2015.
• [59] J. J. Liang, P. N. Suganthan, K. Deb, “Novel composition test functions for numerical global optimization,” In Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS, 2005, pp. 68-75.
• [60] S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Computing and Applications, vol. 27, no. 4, pp. 1053-1073, 2016.
• [61] S. Öztürk, N. Öztürk, “Improvement of Image Enhancement Method Using Artificial Bee Colony Algorithm,” Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 4, no. 4, pp. 173-183, 2016.
• [62] L. Dos Santos Coelho, J. G. Sauer, M. Rudek, “Differential evolution optimization combined with chaotic sequences for image contrast enhancement,” Chaos, solitons & fractals, vol. 42, no. 1, pp. 522-529, 2009.