Approximation of Pareto optima in multiple-objective, shortest-path problems
Operations Research
Journal of the ACM (JACM)
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
On the approximability of trade-offs and optimal access of Web sources
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
On the approximate tradeoff for bicriteria batching and parallel machine scheduling problems
Theoretical Computer Science
Approximating Multiobjective Knapsack Problems
Management Science
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Multiobjective optimization using GAI models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decision making with multiple objectives using GAI networks
Artificial Intelligence
Efficient approximation algorithms for multi-objective constraint optimization
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Interactive algorithm for multi-objective constraint optimization
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Anytime algorithms for biobjective heuristic search
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Dominance rules for the choquet integral in multiobjective dynamic programming
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
In this paper, we propose near admissible multiobjective search algorithms to approximate, with performance guarantee, the set of Pareto optimal solution paths in a state space graph. Approximation of Pareto optimality relies on the use of an epsilon-dominance relation between vectors, significantly narrowing the set of non-dominated solutions. We establish correctness of the proposed algorithms, and discuss computational complexity issues. We present numerical experimentations, showing that approximation significantly improves resolution times in multiobjective search problems.