Swarm Intelligence Optimization : Editorial Survey

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and optimization. Whereas optimization has been popular academic topic for decades, swarm intelligence is relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flock 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), Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC).These are the most popular swarm intelligence metaheuristics for Single Objective and Multi Objective Problems. This paper presents a comprehensive review of the various proposals on PSOs and ABCc for single and multi-objective optimization problems as reported in the specialized literature. As part of this review, we have attempted to identify the main features of each proposal. We have also discussed some of the key issues and sub-issues pertaining to PSO and ABC. In the last part of the paper, we have enlisted some of the topics within this field that we consider to be promising areas of future research. Keywords— Swarm intelligence. Ant Colony Optimization, Particle Swarm Optimization.