Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

2026-03-22

AI Summary

This study addresses the practical challenge in conventional UAV path planning, where efficiency and safety—often managed by separate decision-making entities—are optimized independently despite requiring coordinated multi-objective decision-making. To this end, the paper introduces, for the first time, a dual-decision-maker multi-objective path planning model and develops novel algorithms, namely BPNNIA, BPHEIA, and BPAIMA. These algorithms are grounded in an immune-inspired multi-objective optimization framework that integrates non-dominated neighborhood selection, a hybrid evolutionary mechanism, and an adaptive immune strategy, further enhanced by a negotiation and solution-fusion mechanism between the two decision-makers. Experimental results demonstrate that BPAIMA consistently outperforms benchmark algorithms such as NSGA-II and OptMPNDS in terms of solution quality, balance between the dual objectives, and convergence performance.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.

Problem

• UAV path planning
• biparty multiobjective optimization
• decision-making
• efficiency
• safety

Innovation

• biparty multiobjective optimization
• UAV path planning
• immune-inspired algorithm
• multiobjective evolutionary algorithm
• decision-maker modeling