In this paper, an effective improved co-evolution ant colony optimisation (MSICEAO)
algorithm is presented to solve complex optimisation problem. In the MSICEAO, the multi …

Ant colony optimization (ACO) algorithm is a recent meta-heuristic method inspired by the
behavior of real ant colonies. The algorithm uses parallel computation mechanism and …

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 …

Ant Colony Optimization algorithms have been successfully applied to solve the Traveling
Salesman Problem (TSP). However, they still have a tendency to fall into local optima …

The ant colony optimization (ACO) algorithm is a type of classical swarm intelligence
algorithm that is especially suitable for combinatorial optimization problems. To further …

To solve the problem of falling into local optimum and poor convergence speed in large
Traveling Salesman Problem (TSP), this paper proposes a Pearson correlation coefficient …

Ant Colony Optimization (ACO) is a metaheuristic proposed by Marco Dorigo in 1991 based
on behavior of biological ants. Pheromone laying and selection of shortest route with the …

Abstract Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic
algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired …

In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-
population strategy, co-evolution mechanism, pheromone updating strategy, and …

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 …