The aim of this study is to find the optimal structural geometry of the front crash member of a car of minimal mass that optimally satisfies all operational conditions. The mechanical domains that have been considered are crash, acoustic (dynamic) and static. They are summed up by 9 objective functions, resulting in a 10-objective optimization problem. However, this problem is further turned into minimizing the mass while maximizing the internal energy (crash objective), subject to constraints on the 8 objectives that arise from the acoustic and static domains. The dimension ofthe objective space of this constrained problem is much lower than that ofthe original 10-objective problem. This significantly reduces convergence time, while decreasing decision making efforts among solutions obtained though pareto-based multiobjective optimization. Nevertheless, since the computation of the structural responses is based on a very time-consuming FEM crash analysis, direct computation of the fitness within an evolutionary algorithm is impossible. The response of car front members is computed using an approximative evaluation that had been identified during the BE96-3046 European project (CE)2: Computer Experiments for Concurrent Engineering. Thanks to this approximation, very good results are obtained in a reasonable time using a Pareto elitist evolutionary algorithm based on NSGA-II ideas, combined with an infeasibility objective approach for constraint handling.

A multiobjective evolutionary algorithm for car front end design / RUDENKO O; SCHOENAUER M; BOSIO T; FONTANA R.. - 2310:(2002), pp. 205-216.

A multiobjective evolutionary algorithm for car front end design

FONTANA, ROBERTO
2002

Abstract

The aim of this study is to find the optimal structural geometry of the front crash member of a car of minimal mass that optimally satisfies all operational conditions. The mechanical domains that have been considered are crash, acoustic (dynamic) and static. They are summed up by 9 objective functions, resulting in a 10-objective optimization problem. However, this problem is further turned into minimizing the mass while maximizing the internal energy (crash objective), subject to constraints on the 8 objectives that arise from the acoustic and static domains. The dimension ofthe objective space of this constrained problem is much lower than that ofthe original 10-objective problem. This significantly reduces convergence time, while decreasing decision making efforts among solutions obtained though pareto-based multiobjective optimization. Nevertheless, since the computation of the structural responses is based on a very time-consuming FEM crash analysis, direct computation of the fitness within an evolutionary algorithm is impossible. The response of car front members is computed using an approximative evaluation that had been identified during the BE96-3046 European project (CE)2: Computer Experiments for Concurrent Engineering. Thanks to this approximation, very good results are obtained in a reasonable time using a Pareto elitist evolutionary algorithm based on NSGA-II ideas, combined with an infeasibility objective approach for constraint handling.
9783540435440
Artificial Evolution
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/1876449
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