ABSTRACT
The behaviors of a flock of birds, a group of ants, a school of fish, etc., were the field of study during earlier days. Such collective motion of insects and birds is known to as “swarm behavior.” Later on biologists and computer scientists in the field of artificial life studied the modeling of biological swarms to analyze the interaction among the social animals, to achieve goals, and to evolve them. Recently the interest of engineers is increasing rapidly since the resulting swarm intelligence (SI) is applicable in optimization problems in various fields like telecommunicate systems, robotics, electrical power systems, consumer appliances, traffic patterns in transportation systems, military applications, and many more. In swarm intelligence, N agents in the swarm or a social group are coordinating to achieve a specific goal by their behavior. This kind of collective intelligence arises from large groups of relatively simple agents. The actions of the agents are governed by simple local rules. The intelligent agent group achieves the goal through interactions of the entire group. A type of “self-organization” emerges from the collection of actions of the group. Swarm intelligence is the collective intelligence of groups of simple au-
tonomous agents. The autonomous agent is a subsystem that interacts with its environment, which probably consists of other agents, but acts relatively independently from all other agents. There is no global plan or leader to control the entire group of autonomous agents. Consider for example, the movement of a bird in a flock, the bird adjusts its movements such that it coordinates with the movements of its neighboring flock mates. The bird tries to move along with its flock maintaining its movement along with the others and moves in such a way to avoid collisions among them. There is no leader to assign the movements therefore the birds try to coordinate and move among themselves. Any bird
helps birds take advantage of several things including protection from predators (especially for birds in the middle of the flock), and searching for food (essentially each bird is exploiting the eyes of every other bird). This chapter gives the basic definition of swarms, followed by a de-
scription on Swarm Robots. The Biological Models, Characterizations of Stability, and Overview of Stability Analysis of Swarms are also elaborated in this chapter. The chapter deals with the taxonomy of Swarm Intelligence, properties of the Swarm Intelligence system, studies and applications of swarm intelligence. The variants of SI such as Particle Swarm Optimization (PSO) and Ant Colony Algorithms for Optimization Problems are discussed. A few applications of Particle Swarm Optimization such as Job Scheduling on Computational Grids and Data Mining and a few applications of Ant Colony Optimization such as Traveling Salesman Problem (TSP), Quadratic Assignment Problem (QAP), and Data Mining and their implementation in MATLAB are explained in this chapter.
