This paper describes the use of parallel genetic algorithms to automate the component sizing stage of the design process. Experiences of applying the method to fluid power systems are discussed and problems encountered are highlighted. Refinements to the standard parallel genetic algorithm for use as a single objective optimizer and its adaptation for use as a method to locate Pareto optimal solutions of multi-objective optimization problems are described. The multi-objective algorithm is used to optimize a mathematical problem and two fluid power circuits.
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References
1.
DonneM. S.TilleyD. G.RichardsC. W.Adaptive search and optimization techniques in fluid power system design. Conference on Adaptive computing in engineering design and systems control, University of Plymouth, 21–22 September1994, pp. 67–76.
2.
DonneM. S.Development of an optimization technique to aid fluid power system design. PhD thesis, School of Mechanical Engineering, University of Bath, November 1993.
HollandJ. H.Adaptation in natural and artificial systems, 1975, University of Michigan Press, Ann Arbor).
5.
GoldbergD. E.Genetic algorithms in search optimization and machine learning, 1989, Addison Wesley, Reading, Mass.).
6.
StarkweatherT.WhitleyD.MathiasK.Optimization using distributed genetic algorithms, Lecture notes in computer science, Vol. 496, 1991 (Springer-Verlag, Berlin).
7.
HookeR.JeevesT. A.Direct search solution of numerical and statistical problems. J. Ass. Comp. Mach., 1961, 8, 212–229.
8.
De JongK. A.SpearsW. M.An analysis of the interacting roles of population size and crossover in genetic algorithms, Lecture notes in computer science, Vol. 496, 1991 (Springer).
9.
OsyczkaA.Multicriterion optimization in engineering, 1984, Wiley, New York).
10.
SchafferJ. D.Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of an International Conference on Genetic algorithms and their applications, 1985, pp. 93–100 (Lawrence Earlbaum).
11.
RoyA.WalleniusJ.Nonlinear multiple objective optimization: An algorithm and some theory. Mathematical Programming, 1992, 55, 235–249.
12.
RichardsC. W.TilleyD. G.TomlinsonS. P.BurrowsC. R.Type insensitive integration codes for the simulation of fluid power systems. ASME Winter Annual Meeting, Texas, 1990, paper WA/FPST-6 (ASME, New York).
