International audienceMeta-heuristics are general-purpose methods for global optimization, which take generally inspiration from natural behaviors and phenomena. Among the others, Ant Colony Optimization (ACO) received particular interest in the last years. In this work, we introduce the environment in ACO, for the meta-heuristic to perform amore realistic simulation of the ants' behavior. Computational experiments on instances of the GPS Surveying Problem (GSP) show that the introduction of the environment in ACO allows us to improve the quality of obtained solutions
Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive fea...
A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized with socio-cogniti...
Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Salesman Proble...
International audienceMeta-heuristics are general-purpose methods for global optimization, which tak...
International audienceWe propose a variant on the well-known Ant Colony Optimization (ACO) general f...
Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant ...
Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired...
Ant Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic ap-proach for solving ha...
The Ant Colony Optimisation algorithm framework here-on referred to as ACO is a new algorithmic fram...
Ant colony optimization (ACO) is an evaluational optimization algorithm inspired by pheromone effect...
warm intelligence is a relative-ly new approach to problem solving that takes inspiration from the s...
. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the comm...
Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied...
Abstract. Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Sales...
Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the s...
Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive fea...
A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized with socio-cogniti...
Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Salesman Proble...
International audienceMeta-heuristics are general-purpose methods for global optimization, which tak...
International audienceWe propose a variant on the well-known Ant Colony Optimization (ACO) general f...
Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant ...
Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired...
Ant Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic ap-proach for solving ha...
The Ant Colony Optimisation algorithm framework here-on referred to as ACO is a new algorithmic fram...
Ant colony optimization (ACO) is an evaluational optimization algorithm inspired by pheromone effect...
warm intelligence is a relative-ly new approach to problem solving that takes inspiration from the s...
. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the comm...
Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied...
Abstract. Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Sales...
Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the s...
Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive fea...
A metaheuristic proposed by us recently, Ant Colony Optimization (ACO) hybridized with socio-cogniti...
Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Salesman Proble...