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ORCIDSwarm Intelligence, Volume 14
Volume 14, Number 1, March 2020
- Antoine Ligot
, Mauro Birattari:
Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms. 1-24 - Mohamed S. Talamali, Thomas Bose, Matthew Haire, Xu Xu, James A. R. Marshall, Andreagiovanni Reina:
Sophisticated collective foraging with minimalist agents: a swarm robotics test. 25-56 - Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Lucia Pallottino, Antonio Franchi:
A study on force-based collaboration in swarms. 57-82
Volume 14, Number 2, June 2020
- Vibhu Trivedi, Pushkar Varshney, Manojkumar Ramteke:
A simplified multi-objective particle swarm optimization algorithm. 83-116 - Amir Mirzaeinia, F. Heppner, Mostafa Hassanalian:
An analytical study on leader and follower switching in V-shaped Canada Goose flocks for energy management purposes. 117-141 - Rui Chen, Bernd Meyer, Julian Garcia:
A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation. 143-170
Volume 14, Number 3, September 2020
- Vera A. Kazakova, Annie S. Wu, Gita Reese Sukthankar:
Respecializing swarms by forgetting reinforced thresholds. 171-204 - Majda Moussa, Giovanni Beltrame:
On the robustness of consensus-based behaviors for robot swarms. 205-231 - Annie S. Wu, R. Paul Wiegand, Ramya Pradhan:
Response probability enhances robustness in decentralized threshold-based robotic swarms. 233-258
Volume 14, Number 4, December 2020
- Vinicius Graciano Santos, Anderson Grandi Pires, Reza Javanmard Alitappeh, Paulo A. F. Rezeck, Luciano C. A. Pimenta, Douglas G. Macharet, Luiz Chaimowicz:
Spatial segregative behaviors in robotic swarms using differential potentials. 259-284 - Bernd Bassimir, Manuel Schmitt, Rolf Wanka:
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves. 285-311 - Daniele Proverbio, Luca Gallo, Barbara Passalacqua, Marco Destefanis, Marco Maggiora, Jacopo Pellegrino:
Assessing the robustness of decentralized gathering: a multi-agent approach on micro-biological systems. 313-331
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