Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging
behavior of ants to find approximate solutions to difficult optimization problems. It can be …

” Advances in Bio-inspired Combinatorial Optimization Problems” illustrates several recent
bio-inspired efficient algorithms for solving NP-hard problems. Theoretical bio-inspired …

Recently we proposed an application of ant colony optimization (ACO) to simulate socio-
cognitive features of a population, incorporating perspective-taking ability to generate …

ACO is introduced to think about the image edge detection issues where the purpose is to
evolve the edge information existing in the picture, since it is critical to understand the …

Different hybrid optimization metaheuristics (see the works of Talbi for classification) either
assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a …

We present an application of Ant Colony Optimisation (ACO) to simulate socio-cognitive
features of a population. We incorporated perspective taking ability to generate three …

Ant Colony Optimization (ACO) is used to solve problems with multiple objectives. Various
extensions have been implemented to the traditional approach to improve algorithm …

Ant models are investigated with the purpose of providing a high-quality performing heuristic
for solving the linear ordering problem. Extending the Ant Colony System (ACS) model, the …

The Generalized Traveling Salesman Problem is one of a well known complex
combinatorial optimization problems. Equality-Generalized Traveling Salesman Problem is …

In our recent research, we implemented an enhancement of Ant Colony Optimization
incorporating the socio-cognitive dimension of perspective taking. Our initial results …