Chaotic Genetic Algorithm based on Lorenz Chaotic System for Optimization Problems

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Author(s)

Reza Ebrahimzadeh 1,* Mahdi Jampour 2

1. Department of Computer Science, Zahedan Branch, Islamic Azad University, Zahedan, Iran

2. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2013.05.03

Received: 10 Aug. 2012 / Revised: 4 Dec. 2012 / Accepted: 11 Feb. 2013 / Published: 8 Apr. 2013

Index Terms

Optimization Algorithm, Chaos Genetic Algorithm, Evolutionary Algorithm, Schaffer, Clonalg

Abstract

Very recently evolutionary optimization algorithms use the Genetic Algorithm to improve the result of Optimization problems. Several processes of the Genetic Algorithm are based on 'Random', that is fundamental to evolutionary algorithms, but important defections in the Genetic Algorithm are local convergence and high tolerances in the results, they have happened for randomness reason. In this paper we have prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algorithm to avoid local convergence. The experimental results show that the proposed method is much more efficient in comparison with the traditional Genetic Algorithm for solving optimization problems.

Cite This Paper

Reza Ebrahimzadeh, Mahdi Jampour, "Chaotic Genetic Algorithm based on Lorenz Chaotic System for Optimization Problems", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.5, pp.19-24, 2013. DOI:10.5815/ijisa.2013.05.03

Reference

[1]Ioannis G. Tsoulos: Solving constrained optimization problems using a novel genetic algorithm. Applied Mathematics and Computation (AMC) 208(1):273-283 (2009)

[2]Wei Juan, Wang Ping: Optimization of Fuzzy Rule Based on Adaptive Genetic Algorithm and Ant Colony Algorithm. Computational and Information Sciences (ICCIS), International Conference on 359-362 (2010)

[3]Sun Feng-jie, Tian Ye: Transmission Line Image Segmentation Based GA and PSO Hybrid Algorithm. Computational and Information Sciences (ICCIS), International Conference on 677-680 (2010)

[4]Lili Liu, Qiang Zhang, Xiaopeng Wei: A RGB image encryption algorithm based on DNA encoding and chaos map. Computers & Electrical Engineering (CEE) 38(5):1240-1248 (2012)

[5]Leo Yu Zhang, Chengqing Li, Kwok-Wo Wong, Shi Shu, Guanrong Chen: Cryptanalyzing a chaos-based image encryption algorithm using alternate structure. Journal of Systems and Software (JSS) 85(9):2077-2085 (2012)

[6]Xingyuan Wang, Lin Teng, Xue Qin: A novel colour image encryption algorithm based on chaos. Signal Processing (SIGPRO) 92(4):1101-1108 (2012)

[7]A. Kanso, M. Ghebleh. A fast and efficient chaos-based keyed hash function. Communications in Nonlinear Science and Numerical Simulation, 18 (1) 109:123 (2013)

[8]Hu Yuxia, Zhang Hongtao. Chaos Optimization Method of SVM Parameters Selection for Chaotic Time Series Forecasting. Physics Procedia, 25 (1) 588:594 (2012)

[9]Qinglan Lia, Pengcheng Xu. Estimation of Lyapunov spectrum and model selection for a chaotic time series. Applied Mathematical Modelling, 36 (12) 6090:6099 (2012)

[10]M. Eisencraft, R.D. Fanganiello, J.M.V. Grzybowski, D.C. Soriano, R. Attux, A.M. Batista. Chaos-based communication systems in non-ideal channels. Communications in Nonlinear Science and Numerical Simulation, 17 (12) 4707:4718 (2012)

[11]Ming-Yuan Cheng and Kuo-Yu Huang, Genetic Algorithm-Based Chaos Clustering approach for nonlinear optimization. Journal of Marine Science and Technology, Vol 18, No. 3, pp. 435:441 (2010)

[12]Huimin Jiang, C.K. Kwong, Zengqiang Chen, Y.C. Ysim. Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control. Expert Systems with Applications, 39 (1) 194:201 (2012)

[13]Amir Hossein Gandomi, Gun Jin Yun, Xin-She Yang, Siamak Talatahari. Chaos-enhanced accelerated particle swarm optimization. Communications in Nonlinear Science and Numerical Simulation, 18 (2) 327:340 (2013)

[14]Manuel Martínez, Sergio García-Nieto, Javier Sanchis, Xavier Blasco Ferragud: Genetic algorithms optimization for normalized normal constraint method under Pareto construction. Advances in Engineering Software (AES) 40(4):260-267 (2009) 

[15]Gao Ye, Zheng Tao: Improved genetic algorithms based on chaotic mutation operation and its application. Multimedia Technology (ICMT), 2010 International Conference on 1-3 (2010) 

[16]Mohammad Saleh Tavazoei, Mohammad Haeri: An optimization algorithm based on chaotic behavior and fractal nature. Journal of Computational and Applied Mathematics 206 (2) 1070-1081 (2007)

[17]Qigui Yang, Caibin Zeng: Chaos in fractional conjugate Lorenz system and its scaling attractors. Commun Nonlinear Sci Numer Simulat 15 (12) 4041–4051 (2010)

[18]Awad El-Gohary, Ammar Sarhan: Optimal control and synchronization of Lorenz system with complete unknown parameters. 30 (5) 1122-1132 (2005)

[19]Lifang Yu, Yao Zhao, Rongrong Ni, Ting Li: Improved Adaptive LSB Steganography Based on Chaos and Genetic Algorithm. EURASIP J. Adv. Sig. Proc. (EJASP) 2010 (2010)