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Harvard
Lorentzon, A. och Tengnäs, V. (2017) Optimization of intra-vehicle architecture using amulti-objective genetic algorithm. Göteborg : Chalmers University of Technology (Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, nr: 2017:50).
BibTeX
@mastersthesis{
Lorentzon2017,
author={Lorentzon, Albin and Tengnäs, Viktor},
title={Optimization of intra-vehicle architecture using amulti-objective genetic algorithm},
abstract={Using problem-specific genetic operators, the multi-objective genetic algorithm Non-dominated sorting genetic
algorithm II (NSGA-II) is adapted to the allocation of software components (SWCs) to electric control units
(ECUs) within an automotive architecture. A simulation environment is developed in order to assess the
performance of the allocation within an architecture, and thereby provide the genetic algorithm with objective
and constraint values. The validity of the optimization method is evaluated by generating artificial software
and hardware architectures, and allowing the genetic algorithm to optimize the software allocation. A novel
algorithm for routing signals within the software architecture, based on forming and connecting vehicle
features, is presented.
The optimized Pareto-fronts of small-scale (17 SWCs and 4 ECUs) automotive architectures are compared
to the ground truth through exhaustive search. The average hypervolume ratio is 98.9%, computed over 10
architectures and 100 optimizations, and 48% of the performed optimizations successfully found the entire
true Pareto-front. For the large-scale (250 SWCs and 25 ECUs) architectures, no ground truth can obtained,
and the optimizations are instead evaluated with regard to consistency. In general, the optimization method
quickly finds feasible solutions. However, discrepancies between the approximated Pareto-fronts suggest that
premature convergence sometimes occurs.
Even though the results indicate that the optimization method works as intended and yields satisfactory
results with respect to formulated aims, it is not evident that this method is applicable to the optimization
of real automotive architectures. The true nature of these architectures may be too complicated to be able
to be compressed into a feasibly low number of objectives, which the developed optimization method requires
to perform well.},
publisher={Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola},
place={Göteborg},
year={2017},
series={Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2017:50},
keywords={Automotive architecture, software component, electronic control unit, software allocation, multiobjective optimization, NSGA-II, busload, memory utilization, signal delivery time, feature generation},
}
RefWorks
RT Generic
SR Electronic
ID 251216
A1 Lorentzon, Albin
A1 Tengnäs, Viktor
T1 Optimization of intra-vehicle architecture using amulti-objective genetic algorithm
YR 2017
AB Using problem-specific genetic operators, the multi-objective genetic algorithm Non-dominated sorting genetic
algorithm II (NSGA-II) is adapted to the allocation of software components (SWCs) to electric control units
(ECUs) within an automotive architecture. A simulation environment is developed in order to assess the
performance of the allocation within an architecture, and thereby provide the genetic algorithm with objective
and constraint values. The validity of the optimization method is evaluated by generating artificial software
and hardware architectures, and allowing the genetic algorithm to optimize the software allocation. A novel
algorithm for routing signals within the software architecture, based on forming and connecting vehicle
features, is presented.
The optimized Pareto-fronts of small-scale (17 SWCs and 4 ECUs) automotive architectures are compared
to the ground truth through exhaustive search. The average hypervolume ratio is 98.9%, computed over 10
architectures and 100 optimizations, and 48% of the performed optimizations successfully found the entire
true Pareto-front. For the large-scale (250 SWCs and 25 ECUs) architectures, no ground truth can obtained,
and the optimizations are instead evaluated with regard to consistency. In general, the optimization method
quickly finds feasible solutions. However, discrepancies between the approximated Pareto-fronts suggest that
premature convergence sometimes occurs.
Even though the results indicate that the optimization method works as intended and yields satisfactory
results with respect to formulated aims, it is not evident that this method is applicable to the optimization
of real automotive architectures. The true nature of these architectures may be too complicated to be able
to be compressed into a feasibly low number of objectives, which the developed optimization method requires
to perform well.
PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,
T3 Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2017:50
LA eng
LK http://publications.lib.chalmers.se/records/fulltext/251216/251216.pdf
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