This paper presents a novel hybrid genetic algorithm that has the ability of the genetic algorithms to avoid being trapped at local minimum while accelerating the speed of local search by using the fuzzy simplex algorithm. The new algorithm is labeled the hybrid fuzzy simplex genetic algorithm (HFSGA). Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The HFSGA generally results in a faster convergence toward extremum.
Issue Section:
Technical Papers
1.
Spendley
, W.
, Hext
, G.
, and Himsworth
, F.
, 1962
, “Sequential Application of Simplex Designs in Optimization and Evolutionary Operation
,” Technometrics
, 4
, pp. 441
–461
.2.
Nelder
, J.
, and Mead
, R.
, 1965
, “A Simplex Method for Function Minimization
,” Comput. J.
, 7
, pp. 308
–313
.3.
Rekalitis, V., Ravindaran, A., and Ragsdell, K. M., 1983, Engineering Optimization: Methods and Applications, Wiley-Interscience, New York.
4.
Hedlund
, P.
, and Gustavsson
, A.
, 1999
, “Design and Evaluation of an Effective Modified Simplex Method
,” Anal. Chim. Acta
, 391
, pp. 257
–267
.5.
Trabia
, M.
, and Lu
, X.
, 2001
, “A Fuzzy Adaptive Simplex Search Optimization Algorithm
,” ASME J. Mech. Des.
, 123
, pp. 216
–225
.6.
Gen, M., and Cheng, R., 1997, Genetic Algorithms & Engineering Design, Wiley Interscience, New York.
7.
Ishibuchi
, H.
, Yamamoto
, N.
, Murata
, T.
, and Tanaka
, H.
, 1994
, “Genetic Algorithms and Neighborhood Search Algorithms for Fuzzy Flowshop Problems
,” Fuzzy Sets Syst.
, 67
, pp. 81
–100
.8.
Renders
, J.
and Flasse
, S.
, 1999
, “Hybrid Methods Using Genetic Algorithms for Global Optimization
,” IEEE Trans. Syst. Man Cybern.
, 26
, pp. 243
–258
.9.
Sotiropoulos
, D.
, Stavropoulos
, E.
, and Vrahatis
, M.
, 1997
, “A New Hybrid Genetic Algorithm for Global Optimization
,” Nonlinear Anal. Theory, Methods Appl.
, 30
, pp. 4529
–4538
.10.
Yen
, J.
, Liao
, J.
, and Randolph
, D.
, 1998
, “A Hybrid Approach to Modeling Metabolic Systems Using a Genetic Algorithm and Simplex Method
,” IEEE Trans. Syst. Man Cybern.
, 28
, pp. 173
–191
.11.
Okamoto
, M.
, Nonaka
, T.
, Ochiai
, S.
, and Tominaga
, D.
, 1998
, “Nonlinear Numerical Optimization with Use of a Hybrid Genetic Algorithm Incorporating the Modified Powell Method
,” Appl. Math. Comput.
, 91
, pp. 63
–72
.12.
Jwo
, W.
, Liu
, C.
, and Liu
, C.
, 1999
, “Large-Scale Optimal VAR Planning by Hybrid Simulated Annealing/Genetic Algorithm
,” Int. J. Electr. Power Energy Syst.
, 21
, pp. 39
–44
.13.
Musil
, M.
, Wilmut
, M.
, and Chapman
, N.
, 1999
, “A Hybrid Simplex Genetic Algorithm for Estimating Geoacoustic Parameters Using Matched-Field Inversion
,” IEEE J. Ocean. Eng.
, 24
, pp. 358
–369
.14.
Nassef, A. O., Hegazi, H. A., and Metwalli, S. M., 2000, “A Hybrid Genetic-Direct Search Algorithm for the Shape Optimization of Solid C-frame Cross-Sections,” Proceedings of the 26th Design Automation Conference.
15.
Michalewicz, Z., 1994, Genetic Algorithms+Data Structure=Evolutionary Programs, Springer-Verlag, New York.
16.
Haupt, R., and Haupt, S., 1998, Practical Genetic Algorithms, Wiley Interscience, New York.
17.
Teng, C., and Angeles, J., 1999, “A Sequential Quadratic-Programming Algorithm Using Orthogonal Decomposition with Gerschgorin Stabilization,” Proceedings of the 1999 ASME Design Engineering Technical Conferences.
18.
Lo, C., and Papalambros, P., 1990, “A Deterministic Global Design Optimization Method for Nonconvex Generalized Polynomial Problems,” Advances in Design Automation, ASME, New York, pp. 41–49.
19.
Ostermark
, R.
, 1999
, “Solving a Nonlinear Non-Convex Trim Loss Problem with a Genetic Hybrid Algorithm
,” Computers & Operations Research
, 26
, pp. 623
–635
.Copyright © 2004
by ASME
You do not currently have access to this content.
