International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 6, No. 3, 8 Jun. 2014
Cover page and Table of Contents: PDF (size: 1787KB)
Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems
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Author(s)
Ahmed F. Ali
1,*
Index Terms
Meta-heuristics, Genetic algorithm, Global optimization problems, Local search algorithm
Abstract
In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic algorithm (GA) and local search algorithm, which increase the capability of the algorithm to perform wide exploration and deep exploitation. The proposed algorithm is called a Genetic Local Search Algorithm with Self-Adaptive Population Resizing (GLSASAPR). GLSASAPR employs a self-adaptive population resizing mechanism in order to change the population size NP during the evolutionary process. Moreover, a new termination criterion has been applied in GLSASAPR, which is called population vector (PV ) in order to terminate the search instead of running the algorithm without any enhancement of the objective function values. GLSASAPR has been compared with eight relevant genetic algorithms on fifteen benchmark functions. The numerical results show that the proposed algorithm achieves good performance and it is less expensive and cheaper than the other algorithms.
Cite This Paper
Ahmed F. Ali, "Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.6, no.3, pp.51-63, 2014. DOI:10.5815/ijieeb.2014.03.08
Reference
[1]C.T. Cheng, C.P. Ou, K.W. Chau, Combining a fuzzy optimal model with a genetic algorithm to solve multiobjectiverainfallrunoffmodelcalibration, Journal of Hydrology 268 (14) , 72-86, 2002.
[2]F. Neri, V. Tirronen, Scale factor local search in differential evolution,MemeticComput. J. 1 (2) 153-171, 2009.
[3]S. Kimura, A. Konagaya, High dimensional function optimization using a new genetic local search suitable for parallel computers, in: Proc. IEEE Int. Conf. Syst., Man, and Cybern., vol. 1, pp. 335-342, Oct. 2003.
[4]N. Krasnogor, J.E. Smith, A tutorial for competent memetic algorithms: model, taxonomy, and design issue, IEEE Trans. Evol. Comput. 9 (5), 474-488, 2005.
[5]D. Molina, M. Lozano, F. Herrera, Memetic algorithm with local search chaining for large scale continuous optimization problems, in: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 830-837, 2009.
[6]N. Noman, H. Iba, Accelerating differential evolution using an adaptive 948 local search, IEEE Transactions on Evolutionary Computation 12 (1)949 107-125, 2008.
[7]V. Tirronen, F. Neri, T. Karkkainen, K. Majava, T. Rossi, An enhanced memetic differential evolution in filter design for defect detection in paper production, Evol. Comput. J. 16 (4), 529-555, 2008.
[8]B. Liu, L. Wang, Y.H. Jin, An effective PSO-based memetic algorithm for flow shop scheduling, IEEE Trans. Syst. Man Cybern. 37 (1), 18-27, 2007.
[9]Y. Wang, C. Dang, An evolutionary algorithm for global optimization based on level-set evolution and latin squares, IEEE Transactions on Evolutionary Computation 11 (5), 579-595, 2007.
[10]W. Zhong, J. Liu, M. Xue, L. Jiao, A multiagent genetic algorithm for global numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics-Part B, 34(2), 1128-1141, 2004.
[11]H.K. Birru, K. Chellapilla, S.S. Rao, Local search operators in fast evolutionary programming, in: Proc. of the 1999 Congr. onEvol. Comput.,vol. 2, Jul., pp. 1506-1513, 1999.
[12]J. H. Holland. Adaptation in Natural and Artificial Systems.University of Michigan Press, Ann Arbor, MI, 1975.
[13]A.F. Ali, AE Hassanien, Minimizing molecular potential energy function using genetic Nelder-Mead algorithm, 8th International Conference on Computer Engineering & Systems (ICCES), pp. 177-183, 2013.
[14]T. B¨ ack, D. B. Fogel, and T. Michalewicz. Evolutionary Computation:Basic Algorithms and Operators. Institute of Physics Publishing, 2000.
[15]A. Hedar and A.F. Ali, and T. Hassan, Genetic algorithem and tabu search based methods for molecular 3D-structure prediction, International Journal of Numerical Algebra, Control and Optimization (NACO), 2011.
[16]A. Hedar and A.F. Ali, and T. Hassan, Finding the 3D-structure of a molecule using genetic algorithm and tabu search methods, in: Proceeding of the 10th International Conference on Intelligent Systems Design and Applications (ISDA2010), Cairo, Egypt, 2010.
[17]Z. Yang, K. Tang, X. Yao, Large scale evolutionary optimization using cooperativecoevolution, Information Sciences 178, 2985-2999, 2008.
[18]A. Hedar, A.F. Ali. Tabu search with multi-levelneighborhood structures for high dimensional problems. Appl Intell, 37: 189-206, 2012.
[19]M. Gong, L. Jiao, L. Zhang, Baldwinian learning in clonal selection algorithm for optimization, Information Sciences 180, 1218-1236, 2010.
[20]P. Koro ˜ Sec, J. ˜ Silc, B. Filipic, The differential ant-stigmergy algorithm, Information Sciences 192 , pp. 82-97, 2012.
[21]A. Wright, Genetic Algorithms for Real Parameter Optimization, Foundations of Genetic Algorithms 1, G.J.E Rawlin (Ed.) (Morgan Kaufmann San Mateo), 205-218, 1991.
[22]C.Y. Lee, X. Yao, Evolutionary programming using mutations based on the levy probability distribution, IEEE Transactions on Evolutionary Computation 8 (1), 113, 2004.
[23]X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation 3 (2), 82-102, 1999.
[24]Y.W. Leung, Y.P. Wang, An orthogonal genetic algorithm with quantization for global numerical optimization, IEEE Transactions on Evolutionary Computation 5 (1) , 41-53, 2001.
[25]C.S. Hong, Z. Quan, Integral Global Optimization: Theory, Implementation and Application, Springer-Verlag, Berlin, Germany, 1988.
[26]J.-T. Tsai, T.-K. Liu, J.-H. Chou, Hybrid Taguchi-genetic algorithm for global numerical optimization, IEEE Transactions on Evolutionary Computation 8 365-377, 2004.
[27]G. Garai, B.B. Chaudhurii, A novel hybrid genetic algorithm with Tabu search for optimizing multidimensional functions and point pattern recognition, Inform. Sci, 2012.