A Comparative Analysis of Genetic Algorithms, Particle Swarm Optimization, and Biogeography-Based Optimization for Social Media Influencer Optimization 

Abstract This study concerns a comparative work of how Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Biogeography-Based Optimization helped to enhance the performance of social media influencers. The fitness function combines engagement rate with conversion and audience growth for influencer marketing is presented in this paper. Results Compared Performance Analysis of Algorithms with their Compute Efficiencies in Different Generation In this paper, In this paper we have raised a question of investigating Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Biogeography-Based Optimization method to optimize selection mechanism for social media influencers. In order to measure the effectiveness of influencer approaches, a fitness function which evaluates engagement rates, conversion and audience growth is incorporated so we can determine the best algorithm. We demonstrate that each of these algorithm modes improves the average and best fitness values over multiple generations, although they exhibit significantly different computational cost performances and consistency in their behaviour.