Evolutionary Strategies for Multidisciplinary ... - Dynardo GmbH

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Literature H.-P. Schwefel: Evolution and Optimum Seeking, Wiley, NY, 1995. I. Rechenberg: Evolutionsstrategie 94, frommann-holzboog, Stuttgart, 1994. Th. Bäck: Evolutionary Algorithms in Theory and Practice, Oxford University Press, NY, 1996. Th. Bäck, D.B. Fogel, Z. Michalewicz (Hrsg.): Handbook of Evolutionary Computation, Vols. 1,2, Institute of Physics Publishing, 2000. 30
Multidisciplinary Optimization (MDO) rear impact high speed rear impact low speed Lateral impact Statics MDO Dynamic front impact high speed Front impact low speed Different disciplines involved (crash cases, NVH, statics, …). Very demanding simulation (multiple CPUs, many hours). Large number of parameters and constraints. Very few evaluations (shots) possible (often < 300). 31
Page 1 and 2: Evolutionary Strategies for Multidi
Page 3 and 4: Background I Biology = Engineering
Page 5 and 6: Optimization f : objective function
Page 7 and 8: Dynamic Optimization Dynamic Functi
Page 9 and 10: The Fundamental Challenge Global co
Page 11 and 12: Evolution Strategies 11
Page 13 and 14: Evolution Strategy - Basics Mostly
Page 15 and 16: Evolution Strategy: Algorithms Muta
Page 17 and 18: Operators: Mutation - one σ Thereb
Page 19 and 20: Evolution Strategy Algorithms Selec
Page 21 and 22: Operators: Selection Possible occur
Page 23 and 24: Self-adaptation Self adaptation No
Page 25 and 26: Self-adaptation Self adaptation: :
Page 27 and 28: Mixed-Integer Mixed Integer Evoluti
Page 29: Mixed-Integer Mixed Integer ES: Mut
Page 33 and 34: MDO Crash / Statics / Dynamics Mini
Page 35 and 36: MDO Production Runs (II) Mass NuTec
Page 37 and 38: MDO ASF ® Front Optimization Pre-o
Page 39 and 40: MDO Run Comparison Initial design,
Page 41: Corporate Headquarters: Charlotte,

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