Theory and applications of swarm intelligence

Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In particular, the discipline focuses on the behavior of social insects such as fish schools and bird flocks and colonies of ants, termites, bees, and wasps. The most well-known examples of systems studied by swarm intelligence are particle swarm optimization (PSO) and ant colony optimization (ACO). Particle swarm optimization mimics the behavior of fish schooling and bird flocking. PSO is a population-based stochastic optimization strategy with fast convergent speed than general evolutionary algorithms (EAs). Different from EAs, each particle employs not only position information but also the velocity information. They communicate good positions to each other and adjust their own positions according to their decision. In PSO, a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually, the swarm as a whole, like a flock of birds collectively foraging for food, moves close to an optimum of the fitness function. Ant colony optimization is a probabilistic technique for solving computational hard problems which can be reduced to finding optimal paths. The main idea is the indirect communication between the ants by means of chemical pheromone trails. The special issue on the swarm intelligence is dedicated to the latest work in the theory and applications in this exciting area. Our aim is to provide a useful reference for understanding new trends on swarm intelligence. After a detailed review process, a total of four papers were selected to reflect the call thematic vision. The contents of these studies are briefly described as follows. The first paper entitled ‘Analysis of emergent symmetry breaking in collective decision making’ by Heiko Hamann, Thomas Schmickl, Heinz Worn, and Karl Crailsheim investigates a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent– agent encounters and is inspired by the aggregation behavior of honey bees. In this article, symmetry breaking is defined, several symmetry-breaking measures are proposed, and phenomenon of emergent symmetry breaking within authors’ observed system is reported. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed, and two macroscopic features are determined that significantly influence the symmetry-breaking behavior. In addition, the connection between the ability of the Z. Cui (&) Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024 Shanxi, Taiyuan, China e-mail: cuizhihua@gmail.com