Assessment of multi-objective genetic algorithms with different niching strategies and regression methods for engine optimization and design

Proceedings of the Spring Technical Conference of the ASME Internal Combustion Engine Division

Volumn , Issue , 2009, Pages 487-496

  Shi, Yu ^a   Reitz, Rolf D ^a  

^a University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL RESOURCES; DATA SETS; DESIGN PARAMETERS; DIVERSITY METRICS; ENGINE OPTIMIZATION; EVALUATION TOOL; HYBRID MODE; HYPERCUBE; K-NEAREST NEIGHBORS; KRIGING; LEARNING STRATEGY; LOG TRANSFORMATION; MULTI-OBJECTIVE GENETIC ALGORITHM; NICHING STRATEGIES; NON-DOMINATED SORTING GENETIC ALGORITHMS; NON-LINEAR; NSGA-II; OPTIMAL ACCURACY; OPTIMAL COMBINATION; OPTIMIZATION PROCESS; OPTIMIZATION STUDIES; PREDICTION ACCURACY; RADIAL BASIS FUNCTIONS; REGRESSION METHOD; REGRESSION TECHNIQUES; SWIRL RATIO;

COMBUSTION; DESIGN OF EXPERIMENTS; ENGINES; FUZZY CONTROL; GENETIC ALGORITHMS; INTERNAL COMBUSTION ENGINES; INVERSE KINEMATICS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 77952813885     PISSN: 15296598     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/ICES2009-76015     Document Type: Conference Paper

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