Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Search with Approximate Function Evaluation
Proceedings of the 1st International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Microchannel Optimization Using Multiobjective Evolution Strategies
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An Infeasibility Objective for Use in Constrained Pareto Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
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 of the objective space of this constrained problem is much lower than that of the 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.