Approximation and radial-basis-function networks
Neural Computation
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
Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A comparative study of metamodeling methods for multiobjective crashworthiness optimization
Computers and Structures
Structural and Multidisciplinary Optimization
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Multiobjective optimization design for vehicle occupant restraint system under frontal impact
Structural and Multidisciplinary Optimization
Adaptive heuristic search algorithm for discrete variables based multi-objective optimization
Structural and Multidisciplinary Optimization
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It is important to consider the performances of lightweight, stiffness, strength and rollover safety when designing a bus body. In this paper, the finite element (FE) analysis models including strength, stiffness and rollover crashworthiness of a bus body are first built and then validated by physical tests. Based on the FE models, the design of experiment is implemented and multiple surrogate models are created with response surface method and hybrid radial basis function according to the experimental data. After that, a multi-objective optimization problem (MOP) of the bus body is formulated in which the objective is to minimize the weight and maximize the torsional stiffness of the bus body under the constraints of strength and rollover safety. The MOP is solved by employing multi-objective evolutionary algorithms to obtain the Pareto optimal set. Finally, an optimal solution of the set is chosen as the final design and compared with the original design.