Hierarchical heterogeneous Ant Colony Optimization

Ant Colony Optimization (ACO) is used to solve problems with multiple objectives. Various extensions have been implemented to the traditional approach to improve algorithm performance or quality of solutions. In this paper we propose a novel ACO-based method that involves heterogeneity and hierarchy in the area of automated meal plans. The hierarchy consists of 2 levels: at the first there are ants working in a fairly traditional way (a worker); at the second there is an ant manager. Each worker has its own plan and searches the unique environment. The second level ant monitors a group of workers. Experimental results show that this approach is capable to tackle the task in a reasonable time and quality.