Hierarchical heterogeneous Ant Colony Optimization

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...

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Bibliographic Details
Published in:2012 Federated Conference on Computer Science and Information Systems (FedCSIS) pp. 197 - 203
Main Authors: Rusin, M., Zaitseva, E.
Format: Conference Proceeding
Language:English
Published: IEEE 01-09-2012
Subjects:
Algorithm design and analysis
Ant colony optimization
Monitoring
Optimization
Planning
Robots
Sensitivity
Online Access:Get full text
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Summary: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.
ISBN:9781467307086
1467307084