Evaluation And Selection Of Optimal Mimic Algorithms For Enhanced Information Exchange System

Efficient information exchange is crucial in modern communication networks, requiring robust optimization techniques for enhanced performance.This study evaluates and compares three bio-inspired metaheuristic algorithms-Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), and Firefly Algorithm (FA)-in optimizing key parameters of information exchange systems.PSO offers fast convergence but may struggle with global exploration, GWO balances exploration and exploitation through hierarchical hunting strategies, and FA excels in multimodal optimization but may converge slowly in certain cases.The research aims to determine the most suitable algorithm for optimizing switching, efficiency, scalability, and robustness.A comprehensive performance analysis is conducted based on key metrics, with validation through simulations.The findings provide insights into the strengths and limitations of each algorithm, aiding in the selection of the optimal approach for real-world applications.These biomimic algorithms are analyzed and here in this study contribute to advancing bio-mimic inspired optimization techniques in enhancing information exchange systems.