Ant colony optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants
[13, 9, 14, 11, 15, 8]. In ACO algorithms, artificial ants are probabilistic solution construction …

The indirect communication and foraging behavior of certain species of ants has inspired a
number of optimization algorithms for NP-hard problems. These algorithms are nowadays …

This paper presents a new variant of Ant Colony Optimization (ACO) for the Traveling
Salesman Problem (TSP). ACO has been successfully used in many combinatorial …

Applying parameter adaptation means operating on parameters of an algorithm while it is
tackling an instance. For ant colony optimization, several parameter adaptation methods …

Travelling Salesman Problem (TSP) is one of the main and famous problems in finding the
shortest path. But life is not ideal, so in this paper, we proposed design a Semantic …

Metaheuristics are powerful techniques for solving hard real-world problems in many
application domains. Their behavior and performance strongly depend on their ability to …

ACO R is a well-established ant colony optimization algorithm for continuous-domain
optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's …

Stochastic local search algorithms require finding an appropriate setting of their parameters
in order to reach high performance. The parameter tuning approaches that have been …

This study proposes machine learning strategies to control the parameter adaptation in ant
colony optimization algorithm, the prominent swarm intelligence metaheuristic. The …

Configuring parameter settings for ant colony optimisation (ACO) based algorithms is a
challenging and time consuming task, because it usually requires evaluating a large number …