Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …

ANT COLONY optimization (ACO) is part of a larger field of research termed ant algorithms
or swarm intelligence that deals with algorithmic approaches that are inspired by the …

Although an ant is a simple creature, collectively a colony of ants performs useful tasks such
as finding the shortest path to a food source and sharing this information with other ants by …

Abstract The Ant Colony Optimization (ACO) algorithm is a well-known optimization method
that has been successfully applied to solve many difficult discrete optimization problems. A …

Abstract Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone
trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic …

Ant Colony Optimization (ACO)[2] is a recently proposed metaheuristic approach for solving
hard combinatorial optimization problems. The inspiring source of ACO is the foraging …

Abstract The design of Ant Colony Optimization (ACO) has been inspired by the foraging
behavior of ant colonies. ACO is one of the most widely used metaheuristic algorithms …

Abstract Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics often
applied to finding approximate solutions to difficult optimization problems. Despite being …

In search and rescue operations, an efficient search path, colloquially understood as a path
maximizing the probability of finding survivors, is more than a path planning problem …

Although ant colony optimization (ACO) has successfully been applied to a wide range of
optimization problems, its high time-and space-complexity prevent it to be applied to the …