A Survey on Swarm Intelligence Algorithms?

This paper surveys the intersection of fascinating and increasingly popular domain: swarm intelligence. Swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behaviour that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Most swarm intelligence algorithms were devised for continuous optimization problems. This survey aims at providing an updated review of research of swarm intelligence algorithms for discrete optimization problems, comprising combinatorial or binary. The biological inspiration that stimulated the creation of each swarm algorithm is introduced, and later and encoding methods used to adapt each algorithm for complex problems. Methods are compared for different classes of problems and a critical analysis is provided, pointing to future trends.