Trajectory stabilization of a model car via fuzzy control
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Hybridization of neural and fuzzy systems by a multi agent architecture for motor gearbox control
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Coupling control and human factors in mathematical models of complex systems
Engineering Applications of Artificial Intelligence
Brief paper: How to take into account piecewise constraints in constraint satisfaction problems
Engineering Applications of Artificial Intelligence
Brief paper: Local energy minimization in optimal train control
Automatica (Journal of IFAC)
A fast multi-objective evolutionary algorithm based on a tree structure
Applied Soft Computing
An interactive evolutionary multi-objective optimization and decision making procedure
Applied Soft Computing
The Artificial Intelligence
A multiobjective optimization algorithm for discovering driving strategies
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A multi-objective approach to evolving platooning strategies in intelligent transportation systems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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When driving a vehicle along a given route, several objectives such as the traveling time and the fuel consumption have to be considered. This can be viewed as an optimization problem and solved with the appropriate optimization algorithms. The existing optimization algorithms mostly combine objectives into a weighted-sum cost function and solve the corresponding single-objective problem. Using a multiobjective approach should be, in principle, advantageous, since it enables better exploration of the multiobjective search space, however, no results about the optimization of driving with this approach have been reported yet. To test the multiobjective approach, we designed a two-level Multiobjective Optimization algorithm for discovering Driving Strategies (MODS). It finds a set of nondominated driving strategies with respect to two conflicting objectives: the traveling time and the fuel consumption. The lower-level algorithm is based on a deterministic breadth-first search and nondominated sorting, and searches for nondominated driving strategies. The upper-level algorithm is an evolutionary algorithm that optimizes the input parameters for the lower-level algorithm. The MODS algorithm was tested on data from real-world routes and compared with the existing single-objective algorithms for discovering driving strategies. The results show that the presented algorithm, on average, significantly outperforms the existing algorithms.