Publications

2024

C. Crespi, Cutello V, Pavone M, Zito F. An agent framework to explore pathfinding strategies in maze navigation problem. Le Matematiche. 2024; Vol. LXXIX (2024) – Issue II, pp. 555–583 DOI: 10.4418/2024.79.2.17.
C. Cavallaro, C. Crespi, Cutello V, Pavone M, Zito F. Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem. Algorithms. 2024; 17(2):63. DOI: 10.3390/a17020063.
C. Crespi, M. Pavone. Does a Group’s Size Affect the Behavior of a Crowd? An Analysis Based on an Agent Model. In: Elsenbroich C, Verhagen H, eds. Advances in Social Simulation. ESSA 2023. Springer Proceedings in Complexity. Cham: Springer; 2024. DOI: 10.1007/978-3-031-57785-7_31.

C. Crespi, G. Fargetta, M. Pavone, R. A. Scollo. A sensitivity analysis of parameters in an agent-based model for crowd simulations. Applied Soft Computing. 2023; 146:110684. DOI: 10.1016/j.asoc.2023.110684.
C. Crespi, G. Fargetta, M. Pavone, R. A. Scollo. How a Different Ant Behavior Affects on the Performance of the Whole Colony. In: Di Gaspero L, Festa P, Nakib A, Pavone M, eds. Metaheuristics (MIC 2022). Lecture Notes in Computer Science. Cham: Springer; 2023. DOI: 10.1007/978-3-031-26504-4_14.
C. Crespi, G. Fargetta, M. Pavone, R. A. Scollo. An Agent-Based Model for Crowd Simulation. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_2

C. Crespi, G. Fargetta, M. Pavone, R. A. Scollo, L. Scrimali. An Agent-Based Model to Investigate Different Behaviours in a Crowd Simulation. In: Mernik M, Črepinšek M, Eftimov T, eds. Bioinspired Optimization Methods and Their Applications (BIOMA 2022). Lecture Notes in Computer Science. Cham: Springer; 2022. DOI: 10.1007/978-3-031-21094-5_1.

C. Crespi, G. Fargetta, M. Pavone, R. A. Scollo, L. Scrimali. A Game Theory Approach for Crowd Evacuation Modelling. In: Filipič B, Minisci E, Vasile M, eds. Bioinspired Optimization Methods and Their Applications (BIOMA 2020). Lecture Notes in Computer Science. Cham: Springer; 2020. DOI: 10.1007/978-3-030-63710-1_18.
C. Crespi, R. A. Scollo, M. Pavone. Effects of Different Dynamics in an Ant Colony Optimization Algorithm. In: 2020 7th International Conference on Soft Computing and Machine Intelligence (ISCMI 2020). IEEE; 2020. DOI: 10.1109/ISCMI51676.2020.9311553.

C. Crespi, A.G. Spampinato, R. A. Scollo, M. Pavone. ACOLABS: An Ant Colony for Labyrinth Solving. In: XIV International Workshop on Artificial Life and Evolutionary Computation (WIVACE 2019). Extended Abstract.