An Artificial Intelligence Supported Decision Support System for Multi Criteria Rescuer Assignment in Disaster Response

Beyond a Single Plan: Genetic Algorithms for Flexible Maritime Search and Rescue

In maritime search and rescue (SAR) operations, the estimated location of survivors spreads over time, causing the search area to expand continuously. Deploying limited search and rescue units (SRUs) efficiently is critical, but as the search area grows, the number of possible deployment combinations increases exponentially, making exhaustive search impractical. This study proposes a two-stage metaheuristic-based optimization framework that balances computational efficiency and solution quality. In the first stage, the search area is discretized into a grid using simulated particle diffusion results, and grid cell importance is estimated based on particle distribution to assign limited SRUs to high-priority cells first. In the second stage, SRU deployment within each selected cell is reformulated as a permutation-based matching problem to refine resource allocation. The proposed framework was evaluated across 72 scenarios using genetic algorithm, simulated annealing, particle swarm optimization, and differential evolution. Results show that genetic algorithm consistently outperformed other algorithms under resource-constrained conditions while maintaining high solution diversity, providing multiple high-quality alternatives for practical SAR decision-making.

Immune-enhanced NSGA-II for multi-objective multi-UAV disaster relief material distribution

In disaster-relief logistics, disrupted ground transportation networks and the limited payload and endurance of UAVs make it difficult to allocate emergency materials across geographically dispersed demand points. This study formulates a capacity-constrained bi-objective multi-UAV material distribution problem that simultaneously minimizes total flight distance and workload imbalance. Each demand point is served by one UAV in a single-trip route, and route feasibility is evaluated under payload-capacity and maximum route-distance constraints. To solve this constrained discrete optimization problem, we propose an immune-enhanced NSGA-II algorithm, referred to as INSGA-II. The algorithm represents each solution using an integer assignment vector and a priority vector for route decoding, and integrates immune cloning, stimulation-guided clone allocation, and a linearly decreasing mutation probability to improve the exploration of non-dominated allocation-routing solutions. Comparative experiments were conducted over 30 independent runs against standard NSGA-II, MOEA/D, Weighted-GA, and Weighted-ACO using hypervolume (HV), inverted generational distance (IGD), and runtime as evaluation metrics. INSGA-II achieved the highest mean HV of 0.895 and the lowest mean IGD of 0.184 among the compared algorithms, showing favorable average Pareto-front approximation performance in the tested scenario. Its average runtime was higher than NSGA-II and MOEA/D due to additional immune and repair operations, but lower than Weighted-GA and Weighted-ACO in the tested setting. The results suggest that INSGA-II provides quality-oriented Pareto trade-off solutions for capacity-constrained multi-UAV disaster-relief material distribution.

Survival probability-weighted clustering for UAV-based multi-robot task allocation in disaster response

Multiple Objectives for UAVs Paths Optimization for a Disaster Area Reconnaissance

The benefits of early reconnaissance in the event of emergency response can be invaluable for the success of the emergency teams present in the area. The use of unmanned aerial vehicles (UAVs), which are restricted only by weather conditions, introduces a path-planning optimization requirement. We model a disaster area as a set of rectangular regions, prioritized by the expected number of victims. UAVs, working as a team, provide complete coverage of all regions to gather information about conditions on the ground. This paper proposes a new set of objective functions to maximize the number of detected victims in the earliest stages of the search while minimizing completion time. A new solution representation is introduced together with problem-specific mutation operators. Experimental research using NSGA algorithms provides insight into how the model's features affect solution optimization.

MULTI-AGENT Disaster Management System

A Multi-Agent Disaster Management Simulator that automates the disaster response process using intelligent software agents, machine learning, graph-based routing, and generative artificial intelligence.

Research on multi-objective path planning for UAVs in complex environments based on improved adaptive genetic algorithm

With the popularization of unmanned aerial vehicles (UAVs) in scenarios such as military reconnaissance, logistics transportation, and post-disaster rescue, Generating optimal flight paths that guarantee both safety and timeliness amidst high-density barriers and unknown environmental factors presents a formidable challenge in autonomous navigation. Standard genetic algorithms are often hindered by premature convergence, suboptimal local solutions, and diminished genetic diversity in later iterations. In response to these challenges, we propose an Improved Adaptive Genetic Algorithm (IAGA) that synergizes an adaptive mutation strategy with an elite retention framework.This method first constructs a comprehensive cost model combining path length and obstacle threat potential field based on the grid concept, thereby converting the nonlinear path planning problem into a computable mathematical form; secondly, it designs a linearly decreasing adaptive mutation operator that can dynamically adjust the mutation probability according to the population evolution stage, thus maintaining a strong global search ability in the early stages of the algorithm and strengthening local fine search in the later stages; finally, it introduces an elite retention strategy to ensure that excellent individuals are retained and participate in subsequent evolution, avoiding population degradation. Simulation experiments conducted in a Python environment show that in a complex obstacle environment of 100m × 100m× 100m, the IAGA algorithm can effectively plan the optimal path, and the convergence speed is significantly improved compared to traditional genetic algorithms, verifying the effectiveness and robustness of this method in handling multi-constraint path planning problems.