A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis
Collective decision-making is widely observed in natural organisms, especially insects and animals. In this regard, aggregation represents one of the paramount behaviors, as it can be useful for protecting groups against predators or speeding up the foraging process. In the field of autonomous robot...
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IEEE
2025-01-01
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| author | Paolo Pagliuca Alessandra Vitanza |
| author_facet | Paolo Pagliuca Alessandra Vitanza |
| author_sort | Paolo Pagliuca |
| collection | DOAJ |
| description | Collective decision-making is widely observed in natural organisms, especially insects and animals. In this regard, aggregation represents one of the paramount behaviors, as it can be useful for protecting groups against predators or speeding up the foraging process. In the field of autonomous robotics, aggregation is often studied through various paradigms, with evolutionary algorithms being one of the most widely used tools for evolving this collective behavior. In this work, we compared three modern evolutionary strategies — Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Exponential Natural Evolution Strategies (xNES) and OpenAI Evolutionary Strategy (OpenAI-ES) — for their ability to evolve an aggregation behavior in a swarm of robots. Specifically, we systematically varied the number of agents in the group, the environmental conditions (i.e., the number of target nests) and the parameters tuning the fitness function. Our aim is to verify whether and how the selected methods are effective at addressing the problem. The results we obtained indicate how OpenAI-ES achieves better performance in all the considered scenarios. Furthermore, it displays qualitatively more diverse strategies than the other two methods. |
| format | Article |
| id | doaj-art-70348cd6bb9c4adc92b9adfcad1be44f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-70348cd6bb9c4adc92b9adfcad1be44f2025-08-20T03:49:13ZengIEEEIEEE Access2169-35362025-01-0113727217273510.1109/ACCESS.2025.355434410938154A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral AnalysisPaolo Pagliuca0https://orcid.org/0000-0002-3780-3347Alessandra Vitanza1https://orcid.org/0000-0002-7760-8167Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, ItalyInstitute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, ItalyCollective decision-making is widely observed in natural organisms, especially insects and animals. In this regard, aggregation represents one of the paramount behaviors, as it can be useful for protecting groups against predators or speeding up the foraging process. In the field of autonomous robotics, aggregation is often studied through various paradigms, with evolutionary algorithms being one of the most widely used tools for evolving this collective behavior. In this work, we compared three modern evolutionary strategies — Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Exponential Natural Evolution Strategies (xNES) and OpenAI Evolutionary Strategy (OpenAI-ES) — for their ability to evolve an aggregation behavior in a swarm of robots. Specifically, we systematically varied the number of agents in the group, the environmental conditions (i.e., the number of target nests) and the parameters tuning the fitness function. Our aim is to verify whether and how the selected methods are effective at addressing the problem. The results we obtained indicate how OpenAI-ES achieves better performance in all the considered scenarios. Furthermore, it displays qualitatively more diverse strategies than the other two methods.https://ieeexplore.ieee.org/document/10938154/Aggregationcollective decision-makingevolutionary strategiesbehavioral analysis |
| spellingShingle | Paolo Pagliuca Alessandra Vitanza A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis IEEE Access Aggregation collective decision-making evolutionary strategies behavioral analysis |
| title | A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis |
| title_full | A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis |
| title_fullStr | A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis |
| title_full_unstemmed | A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis |
| title_short | A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis |
| title_sort | comparative study of evolutionary strategies for aggregation tasks in robot swarms macro and micro level behavioral analysis |
| topic | Aggregation collective decision-making evolutionary strategies behavioral analysis |
| url | https://ieeexplore.ieee.org/document/10938154/ |
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