Swarm Intelligence: Past, Present and Future

TL;DR

This paper reviews the evolution, current state, and future prospects of swarm intelligence algorithms, analyzing their properties and connections with self-organization to guide future research in optimization.

Contribution

It offers a comprehensive analysis of SI algorithms, highlighting their characteristics, mathematical foundations, and open challenges, along with future research directions.

Findings

SI algorithms are linked to self-organization principles.

Mathematical and qualitative analyses reveal key properties of SI.

Open problems and future directions are identified.

Abstract

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.