Developing Self-Healing Networks with Bio-Inspired Algorithms: Enhancing Resilience in Modern Networks

Powerful referencing modern communal word webs and the function of apt growth equate call for novel strategies to address and commercialize lightening sophisticated and recondite shield mechanisms. In this research, the idea of selforganised networks is investigated with bio-inspired algorithms, specifically using the method of evolution, immune systems and self-repair strategies observed in living organisms. Through the integration of these algorithms within network management systems we hope to improve the capacity of the networks themselves to self-diagnose and self-heal in the event of failures or interruptions. This self-healing capacity should contribute to the material reduction of down time, minimize external inputs and the general enhancement of the resiliency of today's networks. In this work different bio-inspired algorithms including genetic algorithms, ant colony optimization, and artificial immune systems are analyzed to compare their capability for dynamic selforganized adaptation, self-healing, and resource allocation in face of network changes. The proposed approach is in line with the current day's emergent request for elasticity, resiliency, and expansiveness in computing and especially in networking structures as found in the IoT, 5 G, and cloud computing. Experiences, simulations and case studies from these algorithms show how these concepts can significantly improve network resiliency, offer a viable approach for constructing self-healing system in future generation communication networks. The results of this research is useful for the further enhancement of work related to the field of autonomous network management as well as the construction of smart and self-adaptive network structures.