Multiobjective Design Optimization of Merging Configuration for an Exhaust Manifold of a Car Engine

Authors:
Masahiro Kanazaki;Masashi Morikaw;Shigeru Obayashi;Kazuhiro Nakahashi
Affiliations:
-;-;-;-
Venue:
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Year:
2002

Citing 2
Cited 0

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

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Abstract

Multiobjective design optimization system of exhaust manifold shapes for a car engine has been developed using Divided Range Multiobjective Genetic Algorithm (DRMOGA) to obtain more engine power as well as to achieve less environmental impact. The three-dimensional manifold shapes are evaluated by the unstructured, unsteady Euler code coupled with the empirical engine cycle simulation code. This automated design system using DRMOGA was confirmed to find Pareto solutions for the highly nonlinear problems.