A Memetic Algorithm for Mixed-Integer Optimization Problems

Abstract:

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 2970-2974
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.2970
• Online since: January 2013
• Authors: Yung Chien Lin
• Keywords: Evolutionary Algorithm (EA), Memetic Algorithm, Mixed-Integer Optimization

References

[1] Z. Michalewicz, Genetic Algorithm + Data Structure = Evolution Programs, Springer-Verlag, (1994).

[2] W. E. Hart, N. Krasnogor, and J. E. Smith, Recent Advances in Memetic Algorithms, Springer-Verlag, (2005).

[3] Y. C. Lin, K. S. Hwang, and F. S. Wang, A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems, Computers and Mathematics with Applications, vol. 47, pp.1295-1307, (2004).

DOI: 10.1016/s0898-1221(04)90123-x

[4] Y. C. Lin, Y. C. Lin, and K. L. Su, Production planning based on evolutionary mixed-integer nonlinear programming, ICIC Express Letters, Vol. 4, No. 5(B), pp.1881-1886, (2010).

[5] Y. C. Lin, Y. C. Lin, K. L. Su, and W. C. Chang, Identification of control systems using evolutionary neural networks, ICIC Express Letters, Vol. 5, No. 4(B), pp.1307-1312, (2011).

[6] J. A. Nelder and R. Mead, A simplex method for function minimization, Computer J., Vol. 7, No. 4, pp.308-313, (1965).

DOI: 10.1093/comjnl/7.4.308

[7] E. Sandgren, Nonlinear integer and discrete programming in mechanical design, ASME J. Mechanical Design, vol. 112, pp.223-229, (1990).

DOI: 10.1115/1.2912596

[8] J. F. Fu, R. G. Fenton and W. L. Cleghorn, A mixed integer-discrete-continuous programming method and its application to engineering design optimization, Engineering Optimization, vol. 17, No. 3, pp.236-280, (1991).

DOI: 10.1080/03052159108941075

[9] C. Zhang and H. P. Wang, Mixed-discrete nonlinear optimization with simulated annealing, Engineering Optimization, vol. 21, pp.277-291, (1993).

DOI: 10.1080/03052159308940980

[10] Y. J. Cao and Q. H. Wu, Mechanical design optimization by mixed-variable evolutionary programming, in Proc. IEEE Int. Conf. Evolutionary Computation, Indianapolis, 1997, pp.443-446.