SALSA: Swarm Algorithm Simulator

Joel Beedle; Calum Corrie Imrie; Radu Calinescu

SALSA: Swarm Algorithm Simulator

Joel Beedle^*, Calum Corrie Imrie^*, Radu Calinescu

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Swarm algorithms are being increasingly investigated as potential solutions for addressing distributed, complex problems across various domains. However, developing and testing these algorithms remains challenging due to the lack of robust and flexible testbeds. Moreover, efficiently tuning the parameters of swarm algorithms to suit specific situations is a significant challenge. This artifact paper presents SALSA, a comprehensive and extensible framework designed to streamline
the development and evaluation of swarm algorithms - designed with ease of use in mind. Our testbed enables users to define custom swarm algorithms, drone types, targets to detect, and agent interaction processes. It also allows for dynamic parameter updates, providing instant feedback to optimize algorithm performance. Additionally, the testbed supports both user-defined and automated data collection, ensuring that users can gather relevant data efficiently. Overall, SALSA enhances research effectiveness by reducing the time and effort required to set up and test swarm algorithms.

Original language	English
Title of host publication	5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2024
Publisher	IEEE
Publication status	Published - 20 Sept 2024
Event	IEEE International Conference on Autonomic Computing and Self-Organizing Systems - Aarhus, Denmark Duration: 16 Sept 2024 → 20 Sept 2024 Conference number: 5th

Conference

Conference	IEEE International Conference on Autonomic Computing and Self-Organizing Systems
Abbreviated title	ACSOS 2024
Country/Territory	Denmark
City	Aarhus
Period	16/09/24 → 20/09/24

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

Access to Document

SALSA
This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.
Accepted author manuscript, 823 KBLicence: CC BY

Cite this

@inproceedings{b4d28a3368fe46f4a262c5c117b0a63f,

title = "SALSA: Swarm Algorithm Simulator",

abstract = "Swarm algorithms are being increasingly investigated as potential solutions for addressing distributed, complex problems across various domains. However, developing and testing these algorithms remains challenging due to the lack of robust and flexible testbeds. Moreover, efficiently tuning the parameters of swarm algorithms to suit specific situations is a significant challenge. This artifact paper presents SALSA, a comprehensive and extensible framework designed to streamlinethe development and evaluation of swarm algorithms - designed with ease of use in mind. Our testbed enables users to define custom swarm algorithms, drone types, targets to detect, and agent interaction processes. It also allows for dynamic parameter updates, providing instant feedback to optimize algorithm performance. Additionally, the testbed supports both user-defined and automated data collection, ensuring that users can gather relevant data efficiently. Overall, SALSA enhances research effectiveness by reducing the time and effort required to set up and test swarm algorithms.",

author = "Joel Beedle and Imrie, {Calum Corrie} and Radu Calinescu",

note = "This is an author-produced version of the published paper. Uploaded in accordance with the University{\textquoteright}s Research Publications and Open Access policy. ; IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2024 ; Conference date: 16-09-2024 Through 20-09-2024",

year = "2024",

month = sep,

day = "20",

language = "English",

booktitle = "5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2024",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - SALSA: Swarm Algorithm Simulator

AU - Beedle, Joel

AU - Imrie, Calum Corrie

AU - Calinescu, Radu

N1 - Conference code: 5th

PY - 2024/9/20

Y1 - 2024/9/20

N2 - Swarm algorithms are being increasingly investigated as potential solutions for addressing distributed, complex problems across various domains. However, developing and testing these algorithms remains challenging due to the lack of robust and flexible testbeds. Moreover, efficiently tuning the parameters of swarm algorithms to suit specific situations is a significant challenge. This artifact paper presents SALSA, a comprehensive and extensible framework designed to streamlinethe development and evaluation of swarm algorithms - designed with ease of use in mind. Our testbed enables users to define custom swarm algorithms, drone types, targets to detect, and agent interaction processes. It also allows for dynamic parameter updates, providing instant feedback to optimize algorithm performance. Additionally, the testbed supports both user-defined and automated data collection, ensuring that users can gather relevant data efficiently. Overall, SALSA enhances research effectiveness by reducing the time and effort required to set up and test swarm algorithms.

AB - Swarm algorithms are being increasingly investigated as potential solutions for addressing distributed, complex problems across various domains. However, developing and testing these algorithms remains challenging due to the lack of robust and flexible testbeds. Moreover, efficiently tuning the parameters of swarm algorithms to suit specific situations is a significant challenge. This artifact paper presents SALSA, a comprehensive and extensible framework designed to streamlinethe development and evaluation of swarm algorithms - designed with ease of use in mind. Our testbed enables users to define custom swarm algorithms, drone types, targets to detect, and agent interaction processes. It also allows for dynamic parameter updates, providing instant feedback to optimize algorithm performance. Additionally, the testbed supports both user-defined and automated data collection, ensuring that users can gather relevant data efficiently. Overall, SALSA enhances research effectiveness by reducing the time and effort required to set up and test swarm algorithms.

M3 - Conference contribution

BT - 5th IEEE International Conference on Autonomic Computing and Self-Organizing Systems - ACSOS 2024

PB - IEEE

T2 - IEEE International Conference on Autonomic Computing and Self-Organizing Systems

Y2 - 16 September 2024 through 20 September 2024

ER -