Ant Colony Optimization

• Published in: Intelligent Systems pp. 26-1 - 26-12
• Main Authors: Blum, Christian, López-Ibáñez, Manuel
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2011; Taylor & Francis Group
• Edition: 2
• Subjects: Electronics engineering; Power generation & distribution; Hybrid Metaheuristics; Vehicle Routing Problems; Solution Components; ACO Metaheuristic; MLP Network; Current Partial Solution; Candidate List Strategy; Travelling Salesman Problem; CP Technique; Search Space; Local Pheromone Update; Solution Construction; Pheromone Information; Beam Search; Iterated Local Search; Feasible Candidate Solution; RBF Network; General Purpose Optimizer; Discrete Search Space; ACO; Complete Algorithm; PSO Algorithm; ACO Algorithm; Pheromone Update; Pheromone Values
• ISBN: 1439802831; 9781439802830; 1138071889; 9781138071889
• DOI: 10.1201/9781315218427-26

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.