Swarm Intelligence and Decision-Making: The Role of Swarm Intelligence in Decision

Abstract

Swarm intelligence (SI) represents a paradigm in computational and decision-making processes derived from the collective behaviour of decentralised agents such as insects, birds, and fish. This study explores the comprehensive application and evolution of SI within the decision sciences, emphasising its role in enhancing decision-making across various sectors, including logistics, healthcare, and urban planning. Utilising methodologies like literature review and case study analysis, the research underscores SI's adaptability and efficiency in solving complex, dynamic problems by leveraging collective behaviour, decentralisation, and self-organisation principles. The study highlights the performance of SI algorithms, particularly Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO), in real-world scenarios, as they optimize processes and outputs in diverse environments. Furthermore, the study addresses SI's practical implementations and theoretical implications, discussing the challenges and future prospects for integrating SI more deeply into decision-making frameworks. The results show that SI not only makes decision-making much more efficient, but it also provides strong solutions that can be used in changing situations. This makes it a key approach in the ongoing development of decision sciences.

Keywords: Swarm intelligence, algorithms, decision sciences, Internet of Things, data analysis logistics, global behaviours