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

2025-10-29

AI Summary

This paper addresses the fundamental challenge in multi-objective search (MOS) of simultaneously optimizing multiple conflicting criteria. It systematically establishes MOS’s foundational role in real-world applications—including robot navigation, intelligent transportation, and operations research decision-making. From an interdisciplinary perspective, the paper proposes a unified modeling framework for MOS in complex systems and critically surveys mainstream approaches—namely heuristic search, Pareto-optimal solution methods, and multi-criteria decision analysis—highlighting inherent trade-offs among scalability, solution-set quality, and computational efficiency. The work identifies three key frontiers: (1) online MOS under dynamic and uncertain environments; (2) interpretable preference modeling for human–machine collaboration; and (3) large-language-model–enhanced multi-objective policy generation. Finally, it synthesizes critical open problems, offering a clear roadmap for advancing both theoretical foundations and practical deployment of MOS.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Search
• Intelligent Robots: Multi-Robot Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, reflecting the reality that real-world systems rarely optimize a single measure. This paper surveys developments in MOS while highlighting cross-disciplinary opportunities, and outlines open challenges that define the emerging frontier of MOS

Problem

• Balancing multiple conflicting criteria in planning
• Surveying developments in multi-objective search algorithms
• Addressing cross-disciplinary applications and open challenges

Innovation

• Multi-objective search balances conflicting criteria
• Framework applied in robotics and transportation systems
• Survey outlines emerging directions and challenges