Multi-Objective Search: Algorithms, Applications, and Emerging Directions

Significant advancements in MOS algorithms have been made, building upon early frameworks like MOA*. Recent improvements include more efficient exact approaches like BOA* and improved dominance checks, approximate algorithms like PPA* and A*pex, and parallelization techniques leveraging objective ordering and SIMD instructions. Theoretical studies have also classified vertices into must-expand, maybe-expand, and never-expand categories, extending concepts from single-objective search.

The role of heuristics, particularly the 'ideal point heuristic' and Multi-Value Heuristics (MVH), is crucial for guiding search. Additionally, MOS algorithmic principles are being applied to extensions like MOSSP, MOMDP, and MOO, and to seemingly unrelated problems like Multi-objective Minimum Spanning Tree and Multi-Agent Path Finding.

MOS is being applied across diverse domains, demonstrating its broad applicability. In Automated Design & Synthesis, it's used for retrosynthesis planning and drug discovery, balancing multiple criteria for optimal molecular structures. Multi-modal Journey Planning leverages MOS for efficient route determination combining various transport modes, optimizing time, cost, and comfort.

In Robotics, MOS is essential for autonomous vehicle planning, balancing conflicting objectives like cost, energy, and safety, often within hierarchical rulebooks. These applications highlight MOS's ability to address real-world decision-making challenges where single-objective optimization falls short.