Hybrid Mesh Adaptive Direct Search and Genetic Algorithms Techniques for industrial production systems

Open access

Hybrid Mesh Adaptive Direct Search and Genetic Algorithms Techniques for industrial production systems

In this paper, computational and simulation results are presented for the performance of the fitness function, decision variables and CPU time of the proposed hybridization method of Mesh Adaptive Direct Search (MADS) and Genetic Algorithm (GA). MADS is a class of direct search of algorithms for nonlinear optimization. The MADS algorithm is a modification of the Pattern Search (PS) algorithm. The algorithms differ in how the set of points forming the mesh is computed. The PS algorithm uses fixed direction vectors, whereas the MADS algorithm uses random selection of vectors to define the mesh. A key advantage of MADS over PS is that local exploration of the space of variables is not restricted to a finite number of directions (poll directions). This is the primary drawback of PS algorithms, and therefore the main motivation in using MADS to solve the industrial production planning problem is to overcome this restriction. A thorough investigation on hybrid MADS and GA is performed for the quality of the best fitness function, decision variables and computational CPU time.

C. Audet and J.E. Dennis: Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 17(1), (2007), 188-217.

C. Audet: Convergence results for pattern search algorithms are tight. Optimization and Engineering, 5(2), (2004), 101-122.

M.A. Abramson, C. Audet and J.E. Dennis: Generalized pattern searches with derivative information. Mathematical Programming, 100 (2004), 3-25.

C. Audet and J.E. Dennis:. Analysis of generalized pattern searches. SIAM Journal on Optimization, 13(3), (2003), 889-903.

S.J. Chapman: MATLAB programming for engineers. USA: Brooks & Cole, 2002.

I.D. Coope and C.J. Price: On the convergence of grid-based methods for unconstrained optimization. SIAM Journal on Optimization, 11(4), (2001), 859-2001.

C. Davis: Theory of positive linear dependence. American J. of Mathematics, 76 (1954), 733-746.

A. Gilat: MATLAB. An introduction with applications. USA, John Wiley & Sons, Inc., 2005.

M. Gen and R. Cheng:. Genetic Algorithms and Engineering Design. New York, Wiley, 1996.

W. Honggang and Z. Jianchao: The hybrid genetic algorithm for solving nonlinear programming. IEEE Int. Conf. on Intelligent Processing Systems. Beijing, China, (1997).

D.E. Goldberg:. Genetic Algorithms in search optimization and machine learning. Toronto, Addison Wesley, 1989.

F. Jiménez, J.M. Cadenas, G. Sánchez, A.F. Gómez-Skarmeta and J.L. Verdegay:. Multi-objective evolutionary computation and fuzzy optimization. Int. J. of Approximate Reasoning, 43 (2006), 59-75.

MATLAB user's Guide. The MathWorks, 2007.

P. Vasant and N. Barsoum: Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function. Engineering Applications of Artificial Intelligence, 22(4-5), (2009), 767-777.

P. Vasant:. Hybrid simulated annealing and genetic algorithms for industrial production management problems. Int. J. of Computational Methods, 7(2), (2010), 279-297.

P. Vasant and N. Barsoum: Hybrid pattern search and simulated annealing for fuzzy production planning problems. Computers and Mathematics with Application, 60(4), (2010), 1058-1067.

Archives of Control Sciences

The Journal of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2016: 0.705

CiteScore 2016: 3.11

SCImago Journal Rank (SJR) 2016: 0.231
Source Normalized Impact per Paper (SNIP) 2016: 0.565

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 219 153 6
PDF Downloads 86 69 7