Ant Colony Optimization
Ants are capable of finding the shortest path between the food and the colony using a pheromone-laying mechanism. ACO is a metaheuristic optimization approach inspired by this foraging behavior of ants. This chapter is dedicated to ACO.
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Ants are capable of finding the shortest path between the food and the colony using a pheromone-laying mechanism. ACO is a metaheuristic optimization approach inspired by this foraging behavior of ants. This chapter is dedicated to ACO.
11.1 Introduction Eusociality has evolved independently among the hymenoptera insects (ants and bees), and among the isoptera insects (termites). These two orders of social insects have almost identical social structures: populous colonies consisting of sterile workers, often differentiated into castes that are the offspring of one or a few reproductively competent individuals. This type of social structure is similar to a superorganism, in which the colony has many attributes of an organism, including physiological and structural differentiation, coordinated and goal-directed action. Many species of ants have foraging behavior. The strategies of two types of ponerine ant are the army ant style foraging of the genus Leptogenys and the partitioned space search of Pachycondyla apicalis. Termite swarms are organized through a complex language of tactile and chemical signals between individual members. These drive the process of recruitment in response to transient perturbation of the environment. A termite can either experience a perturbation directly, or is informed of it by other termites. The structures as well as their construction of the mound of Macrotermes have been made clear in [22]. Swarm cognition in these termites is in the form of extended cognition, whereby the swarm’s cognitive abilities arise both from interaction among agents within a swarm, and from the interaction of the swarm with the environment, mediated by the mound’s dynamic architecture.
© Springer International Publishing Switzerland 2016 K.-L. Du and M.N.S. Swamy, Search and Optimization by Metaheuristics, DOI 10.1007/978-3-319-41192-7_11
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11 Ant Colony Optimization
Ants are capable of finding the shortest path between the food and the colony (nest) due to a simple pheromone-laying mechanism. Inspired by the foraging behavior of ants, ACO is a metaheuristic approach for solving discrete or continuous optimization problems [1,2,4–6]. Unlike in EAs, PSO and multiagent systems where agents do not communicate with each other, agents in ant-colony system communicate with one another with pheromone. The optimization is the result of the collective work of all the ants in the colony. Ants use their pheromone trails as a medium for communicating information. All the ants secrete pheromone and contribute to the pheromone reinforcement, and old trails will vanish due to evaporation. The pheromone builds up on the traversed links between nodes. An ant selects a link probabilistically based on the intensity of the pheromone. Ant-Q [3,8] merges ant-colony system with reinforcement learning such as Q-learning to update the amount of pheromone on the succeeding link. Ants in the ant-colony system use only one kind of pheromone for their communication, while natural ants also use haptic information for comm
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