Add to ORKGAnt Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic ap-proach for solving hard combinatorial optimization problems. The inspiring source of ACO is the pheromone trail laying and following behavior of real ants which use pheromones as a communication medium. In analogy to the biological example
Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited...
The Ant Colony Optimisation algorithm framework here-on referred to as ACO is a new algorithmic fram...
Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distri...
Ant Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic ap-proach for solving ha...
Ant colony optimization (ACO) is an evaluational optimization algorithm inspired by pheromone effect...
. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the comm...
Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the common...
Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies...
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (AC...
Ant colony optimization (ACO) is a multi-agent heuristic for solving combina-torial optimization pro...
Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied...
Ant Colony Optimization (ACO) is an optimization al-gorithm imitating ant’s feeding action and is ef...
Ant Colony Optimization (ACO) is a biologically inspired optimization algorithm with pheromone ef-fe...
The ant colony optimization (ACO) metaheuristic was inspired from the foraging behaviour of real ant...
This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization wh...
Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited...
The Ant Colony Optimisation algorithm framework here-on referred to as ACO is a new algorithmic fram...
Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distri...
Ant Colony Optimization (ACO) [31, 32] is a recently proposed metaheuristic ap-proach for solving ha...
Ant colony optimization (ACO) is an evaluational optimization algorithm inspired by pheromone effect...
. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the comm...
Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the common...
Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies...
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (AC...
Ant colony optimization (ACO) is a multi-agent heuristic for solving combina-torial optimization pro...
Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied...
Ant Colony Optimization (ACO) is an optimization al-gorithm imitating ant’s feeding action and is ef...
Ant Colony Optimization (ACO) is a biologically inspired optimization algorithm with pheromone ef-fe...
The ant colony optimization (ACO) metaheuristic was inspired from the foraging behaviour of real ant...
This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization wh...
Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited...
The Ant Colony Optimisation algorithm framework here-on referred to as ACO is a new algorithmic fram...
Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distri...