ABSTRACT
This chapter introduces the basic concepts of Ant colony optimization (ACO) without presuming any elaborate knowledge of computer science. ACO was one of the first techniques for approximate optimization inspired by the collective behavior of social insects. From the perspective of operations research, ACO algorithms belong to the class of metaheuristics. At the same time, ACO algorithms are part of a research field known as swarm intelligence. ACO takes its inspiration from the foraging behavior of ant colonies. Many practical problems in logistics, planning, design, engineering, biology, and other fields can be modeled as optimization problems, where the goal is the minimization of a particular objective function. The objective function assigns an objective cost value to each possible candidate solution. The domain of the objective function is called the search space, which may be either discrete or continuous. Optimization problems with discrete search space are also called combinatorial optimization problems.
