Best-First vs. Depth-First AND/OR Search for Multi-objective Constraint Optimization

In this paper we present and evaluate the power of best-first search over AND/OR search spaces for multi-objective constraint optimization. The main virtue of the AND/OR representation of the search space is its sensitivity to problem structure, which can translate into significant time savings. We introduce a linear-space best-first search algorithm that explores an AND/OR search tree and uses a class of partitioning-based heuristics for guidance. The superiority of the best-first approach over depth-first AND/OR Branch-and-Bound search using the same heuristic function is demonstrated empirically on random and real-world benchmarks for multi-objective constraint optimization.