An Agent-Based Model to Investigate Different Behaviours in a Crowd Simulation

Crespi, Carolina; Fargetta, Georgia; Pavone, Mario; Scollo, Rocco A.

doi:10.1007/978-3-031-21094-5_1

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Beyond optimization, ACO demonstrates utility in modeling crowd dynamics. In [32], the authors translated ACO rules into conceptual frameworks rooted in social dynamics. They proposed an agent-based model to study collective crowd behaviors.…”

Section: The Aco Paradigm and Applicationsmentioning

confidence: 99%

Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem

Cavallaro,

Crespi,

Cutello

et al. 2024

Algorithms

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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.

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Section: The Aco Paradigm and Applicationsmentioning

confidence: 99%