Iterative Deepening Multiobjective A

Many real-world optimization problems involve multiple objectives which are often conflicting. When conventional heuristic search algorithms such as A* and IDA* are used for solving such problems, then these problems have to be modeled as simple cost minimization or maximization problems. The task of modeling such problems using a single valued criterion has often proved difficult [6]. The problems involved in accurately and confidently determining a scalar valued criterion on which to base the selection of a most preferred alternative have led to the development of the multiobjective approach to alternative selection [7]. In [7], Stewart and White have presented a multiobjective generalization of the popular A* algorithm, the MOA* , which uses heuristic based best first search techniques to generate all nondominated solutions. Like A*, MOA* also has exponential space complexity. Depth first search techniques use linear space, but take too much time and do not lead to an admissible algorithm. A depth first version of A* called iterative deepening A* (IDA* ) [2] takes linear space and is shown