Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An Evolutionary Algorithm for Integer Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Evolutionary Multiobjective Design in Automotive Development
Applied Intelligence
| Hi-index | 0.00 |
Adaptive Cruise Control (ACC) systems represent an active research area in the automobile industry. The design of such systems typically involves several, possibly conflicting criteria such as driving safety, comfort and fuel consumption. When the different design objectives cannot be met simultaneously, a number of non-dominated solutions exists, where no single solution is better than another in every aspect. The knowledge of this set is important for any design decision as it contains valuable information about the design problem at hand.In this paper we approximate the non-dominated set of a given ACC-controller design problem for trucks using multi-objective evolutionary algorithms (MOEAs). Two different search strategies based on a continuous relaxation and on a direct representation of the integer design variables are applied and compared to a grid search method.