Swarm Intelligence Algorithms for Optimization: Analyzing swarm intelligence algorithms and their applications in optimization problems in artificial intelligence

Abstract

Swarm intelligence algorithms are inspired by the collective behavior of social insects and other animal societies. These algorithms have gained popularity in the field of artificial intelligence (AI) due to their ability to solve complex optimization problems efficiently. This paper provides an overview of swarm intelligence algorithms, including ant colony optimization, particle swarm optimization, and bee colony optimization. It discusses the underlying principles of these algorithms and explores their applications in various optimization problems in AI, such as feature selection, neural network training, and data clustering. The paper also examines the advantages and limitations of swarm intelligence algorithms and discusses future research directions in this field.