Paper Title
Swarm Intelligence-Based Optimal Path Planning for Search and Rescue Operations

Abstract
Search and Rescue (SAR) operations require rapid, reliable, and energy-efficient navigation within hazardous and dynamically constrained environments. In this study, swarm intelligence-based optimal path planning is formulated as a discrete multi-objective optimisation problem within a two-dimensional 50×50 grid-based disaster environment containing 20% randomly distributed obstacles, five victim locations, and multiple autonomous drones with eight-directional movement capability. Standalone Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) algorithms are implemented and comparatively evaluated against a structured sequential Hybrid PSO-ACO framework. A weighted fitness function integrating path length, energy consumption, mission time, and collision penalties, with an additional penalty (λ = 500) for unreached victims, is employed to ensure mission feasibility and operational safety. Twenty independent simulation trials under identical conditions are conducted to ensure statistical robustness. Results demonstrate that the hybrid framework achieves superior performance, yielding a mean fitness value of 63 compared to 124 for PSO and 87 for ACO, representing an improvement of approximately 27-38% with moderate stability (standard deviation ≈14). The findings theoretically substantiate the importance of structured exploration-exploitation balance and practically demonstrate the effectiveness of hybrid swarm intelligence in enhancing SAR path planning efficiency within simulated disaster environments.This work supports efficient and sustainable disaster response systems. Keywords - Swarm Intelligence, Search and Rescue, Particle Swarm Optimization, Ant Colony Optimization, Hybrid Optimization, Multi-objective Path Planning, UAV Navigation, Disaster Simulation.