Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Swarm Intelligence-Based Multi-Agent Systems for Dynamic Disaster Response Management

Authors
Shrinivas Ambala1, *, Chetan Chauhan2, Satpalsing Devising Rajput3, Amol Patil4, Amol Dhumane5, Sujit N. Deshpande6
1Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India
2Department of Computer engineering, Vishwakarma University, Pune, India
3Department of Computer engineering, Pimpri Chinchwad University, Pune, India
4Computer Engineering Department, Vishwakarma Institute of Technology, Pune, India
5Department of Computer engineering, Symbiosis Institute of Technology, Pune, India
6Department of Computer engineering, Vishwakarma University, Pune, India
*Corresponding author. Email: ambala.srinivas@pccoepune.org
Corresponding Author
Shrinivas Ambala
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_66How to use a DOI?
Keywords
Swarm Intelligence; Multi-Agent Systems; Disaster Response; Real-Time Coordination; Edge Computing; Search and Rescue
Abstract

The frequency with which disasters of both natural and human-made causes are increasing implies the need of intelligent, versatile, and resilient response systems. The conventional centralized disaster management systems are not very scalable and are slow, and also, highly vulnerable to infrastructure failure, leading to slow delivery of the necessary healthcare. The article proposed Multi- Agent System (MAS) of Swarm Intelligence to respond to the disaster in hospitals. The framework is an amalgamation of the independent actors, like drones, ground robots, and IoT sensors, which collaborate with the help of biologically inspired algorithms, like ant colony optimization and particle swarm optimization. These adaptation algorithms allow one to make decisions within the least amount of time possible, to allocate tasks and resources in a changing environment. Fusion of real time data and self managing communication protocols further improves coordination and situation awareness despite the fact that degraded communication networks are in use. The strategies that are centralized are far less responsive (temporal), scalable, and robust, as an experiment that is done by simulation. The modular architecture is also compatible with the 6G, blockchain and edge computing as the new technology to provide safe, reliable, and low-latency data transfer words.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_66How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shrinivas Ambala
AU  - Chetan Chauhan
AU  - Satpalsing Devising Rajput
AU  - Amol Patil
AU  - Amol Dhumane
AU  - Sujit N. Deshpande
PY  - 2026
DA  - 2026/01/06
TI  - Swarm Intelligence-Based Multi-Agent Systems for Dynamic Disaster Response Management
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 962
EP  - 974
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_66
DO  - 10.2991/978-94-6463-948-3_66
ID  - Ambala2026
ER  -