Affiliation:
1. Department of Mathematics and Computer Science, University of Catania, v.le Andrea Doria 6, 95125 Catania, Italy
Abstract
This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid while minimizing path costs. Three distinct analyses were conducted: an overall analysis assessing colony performance across different-sized groups, a group analysis examining the performance of each partitioned group, and a pheromone distribution analysis discerning correlations between temporal pheromone distribution and ant navigation. From the results, a dynamic correlation emerged between the degree of colony partitioning and solution quality within the ACO algorithm framework.
Funder
Future Artificial Intelligence Research
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference43 articles.
1. Inequality as a driver of migration: A social network analysis;Plotnikova;Popul. Space Place,2022
2. McAndrew, D. (2021). The Social Psychology of Crime, Routledge.
3. A network analysis on country and financial center attractiveness: Evidence from Asian economies, 2001–2018;Miyakoshi;Int. Rev. Econ. Financ.,2023
4. Engel, J., Nardo, M., and Rancan, M. (2021). Data Science for Economics and Finance: Methodologies and Applications, Springer International Publishing.
5. Network analysis of international export pattern;Soc. Netw. Anal. Min.,2022