Filter-based genetic algorithm for mixed variable programming

Abdel-Rahman Hedar; Alaa Fahim

doi:10.3934/naco.2011.1.99

Article Contents

2011, Volume 1, Issue 1: 99-116. Doi: 10.3934/naco.2011.1.99

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Filter-based genetic algorithm for mixed variable programming

Abdel-Rahman Hedar^1, and
Alaa Fahim^2,

1.
Dept. of Computer Science, Faculty of Computers and Information, Assiut University, Assiut 71526
2.
Dept. of Mathematics, Faculty of Science, Assiut University, Assiut 71516

Received: October 2010

Revised: November 2010

Published: February 2011

Abstract / Introduction Related Papers Cited by

Abstract

In this paper, Filter Genetic Algorithm (FGA) method is proposed to find the global optimal of the constrained mixed variable programming problem. The considered problem is reformulated to take the form of optimizing two functions, the objective function and the constraint violation function. Then, the filter set methodology [5] is applied within a genetic algorithm framework to solve the reformulated problem. We use pattern search as local search to improve the obtained solutions. Moreover, the gene matrix criteria [10] has been applied to accelerated the search process and to terminate the algorithm. The proposed method FGA is promising compared with some other methods existing in the literature.

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Mathematics Subject Classification: Primary: 90C11, 68T20, 68W20; Secondary: 68T05.

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References

[1]	C. Audet and J. E. Dennis, Jr., Analysis of generalized pattern searches, SIAM Journal on Optimization, 13(3) (2003), 889-903.doi: 10.1137/S1052623400378742.
[2]	J. E. Baker, Adaptive selection methods for genetic algorithms, In "Proceedings of the First International Conference on Genetic Algorithms''(eds. J. J. Grefenstette), Lawrence Erlbaum Associates, Hillsdale, MA (1985), 101-111.
[3]	T. Butter, F. Rothlauf, J. Grahl, T. Hildenbrand and J. Arndt, Developing Genetic Algorithm and Mixed Integer Linear Programs for finding optimal strategies for a student's activity, (2006).
[4]	K. Deep, K. P. Singh, M. L. Kansal and C. Mohan, A real coded genetic algorithm for solving integer and mixed integer optimization problems, Mathematics and Computation, 212 (2009), 505-518.
[5]	R. Fletcher and S. Leyffer, Nonlinear programming without a penalty function, Mathematical Programming, 91 (2002), 239-269.doi: 10.1007/s101070100244.
[6]	Genetic Algorithm and Direct Search Toolbox, MATLAB.
[7]	A. Hedar and M. Fukushima, Minimizing multimodal functions by simplex coding genetic algorithm, Optimization Methods and Software, 18 (2003), 265-282.
[8]	A. Hedar and M. Fukuhima, Derivative-Free filter simulated annealing method for constrained continuous global optimization problems, Journal of Global Optimization, 35 (2006), 521-549.doi: 10.1007/s10898-005-3693-z.
[9]	A. Hedar and M. Fukuhima, Evolution strategies learned with automatic termination criteria, Proceedings of SCIS & ISIS 2006, Tokyo, Japan, September 20-24, 2006.
[10]	A. Hedar, B. T. Ong and M. Fukushima, Genetic algorithms with automatic accelerated termination, Technical Report 2007-002, Department of Applied Mathematics and Physics, Kyoto University (January 2007).
[11]	F. Herrera, M. Lozano and J. L. Verdegay, Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis, Artificial Intelligence Review, 12 (1998), 265-319.doi: 10.1023/A:1006504901164.
[12]	R. Hooke and T. A. Jeeves, Direct search solution of numerical and statistical problems, J. ACM, 8 (1961), 212-229.doi: 10.1145/321062.321069.
[13]	Z. Hua and F. Huang, An effective genetic algorithm approach to large scale mixed integer programming problems, Applied Mathematics and Computation, 174 (2006), 897-909.doi: 10.1016/j.amc.2005.05.017.
[14]	Y. C. Lin and K. S. Hwang, A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems, Computers and Mathematics with Applications, 47 (2004), 1295-1307.doi: 10.1016/S0898-1221(04)90123-X.
[15]	A. K. Maiti, A. K. Bhunia and M. Maiti, An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two stage multi-item inventory model with discount policy, Applied Mathematics and Computation, 183 (2006), 903-915.doi: 10.1016/j.amc.2006.05.141.
[16]	M. Schlutera, J. Egeab and J. Bangab, Extended ant colony optimization for non-convex mixed integer nonlinear programming. Computers and Operations Research, 36 (2009), 2217-2229.doi: 10.1016/j.cor.2008.08.015.
[17]	V. K. Srivastava and A. Fahim, An optimization method for solving mixed discrete-continuous programming problems, Computers and Mathematics with Applications, 53 (2007), 1481-1491.doi: 10.1016/j.camwa.2007.01.006.
[18]	T. Yokota, M. Gen and Y. X. Li, Genetic algorithm for nonlinear mixed integer programming problems and its application, Computers and Industrial Engineering, 30 (1996), 905-917.doi: 10.1016/0360-8352(96)00041-1.
[19]	V. Torczon, On the convergence of pattern search algorithms, SIAM J. Optim., 7 (1997), 1-25.doi: 10.1137/S1052623493250780.