Swarm Intelligence Journal Publications [ bib-all ]
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Volume 16 [ bib-vol16 ]
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Number 4 / December 2022 [ bib-vol16-num4 ]
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[1]
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F. d’Amore and A. Clementi and E. Natale.
Phase transition of a nonlinear opinion dynamics with noisy interactions.
Swarm Intelligence, 16(4):261-304, 2022.
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[2]
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O. I. Oduntan and P. Thulasiraman.
Blending multiple algorithmic granular components: a recipe for clustering.
Swarm Intelligence, 16(4):305-349, 2022.
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Number 3 / September 2022 [ bib-vol16-num3 ]
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[1]
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S. K. Lee.
Distributed deformable configuration control for multi-robot systems with low-cost platforms.
Swarm Intelligence, 16(3):169-209, 2022.
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[2]
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R. Miletitch and A. Reina and M. Dorigo and V. Trianni.
Emergent naming conventions in a foraging robot swarm.
Swarm Intelligence, 16(3):211-232, 2022.
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[3]
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H. S. Shin, and T. Li and H. I. Lee, and A. Tsourdos.
Sample greedy based task allocation for multiple robot systems.
Swarm Intelligence, 16(3):233-260, 2022.
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Number 2 / June 2022 [ bib-vol16-num2 ]
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[1]
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A. Costanzo and H. Hildenbrandt and C. K. Hemelrijk.
Causes of variation of darkness in flocks of starlings, a computational model.
Swarm Intelligence, 16(2):91-105, 2022.
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[2]
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J. Patel and P. Sonar and C. Pinciroli.
On multi-human multi-robot remote interaction: a study of transparency, inter-human communication, and information loss in remote interaction.
Swarm Intelligence, 16(2):107-142, 2022.
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[3]
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P. Joćko and B. M. Ombuki-Berman and A. Engelbrecht.
Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems.
Swarm Intelligence, 16(2):143-168, 2022.
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Number 1 / March 2022 [ bib-vol16-num1 ]
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[1]
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C. Aranha and C. L. Camacho Villalón and F. Campelo and M. Dorigo and R. Ruiz and M.
Sevaux and K. Sörensen and T. Stützle.
Metaphor-based metaheuristics, a call for action: the elephant in the room.
Swarm Intelligence, 16(1):1-6, 2022.
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[2]
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F. Jiang and H. Cheng and G. Chen.
Collective decision-making for dynamic environments with visual occlusions.
Swarm Intelligence, 16(1):7-27, 2022.
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[3]
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T. Nguyen and B. Banerjee.
Reinforcement learning as a rehearsal for swarm foraging.
Swarm Intelligence, 16(1):29-58, 2022.
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[4]
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A. Hussein and S. Elsawah and E. Petraki and H. A. Abbass.
A machine education approach to swarm decision-making in best-of-n problems.
Swarm Intelligence, 16(1):59-90, 2022.
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Volume 15 [ bib-vol15 ]
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Number 4 / December 2021 [ bib-vol15-num4 ]
Special Issue: ANTS 2020 Special Issue. Guest Editors: Marco Dorigo, Thomas Stützle, Maria J. Blesa Aguilera, Christian Blum, Heiko Hamann and Mary Katherine Heinrich
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[1]
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M. Dorigo and T. Stützle and M. J. Blesa and C. Blum and H. Hamann and M. K. Heinrich.
ANTS 2020 special issue: Editorial.
Swarm Intelligence, 15(4):311-313, 2021.
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[2]
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G. De Masi and J. Prasetyo and R. Zakir and N. Mankovskii and E. Ferrante and E. Tuci.
Robot swarm democracy: the importance of informed individuals against zealots.
Swarm Intelligence, 15(4):315-338, 2021.
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[3]
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N. Coucke and M. K. Heinrich and A. Cleeremans and M. Dorigo.
HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective.
Swarm Intelligence, 15(4):339-376, 2021.
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[4]
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Q. Shan and S. Mostaghim.
Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing.
Swarm Intelligence, 15(4):377-402, 2021.
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[5]
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J. Bremer and S. Lehnhoff.
Ant colony optimization for feasible scheduling of step-controlled smart grid generation.
Swarm Intelligence, 15(4):403-425, 2021.
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[6]
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J. Nauta and Y. Khaluf and P. Simoens.
Resource ephemerality influences effectiveness of altruistic behavior in collective foraging.
Swarm Intelligence, 15(4):427-457, 2021.
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Number 3 / September 2021 [ bib-vol15-num3 ]
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[1]
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D. H. Stolfi, M. R. Brust, G. Danoy and B. Pascal.
CONSOLE: intruder detection using a UAV swarm and security rings.
Swarm Intelligence, 15(3):205-235, 2021.
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[2]
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K. A. Roundtree, J. R. Cody, J. Leaf, H. O. Demirel and J. A. Adams.
Human-collective visualization transparency.
Swarm Intelligence, 15(3):237-286, 2021.
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[3]
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W. Shan and S. Mstaghim.
Achieving task allocation in swarm intelligence with bi-objective embodied evolution.
Swarm Intelligence, 15(3):287-310, 2021.
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Number 1-2 / June 2021 [ bib-vol15-num1-2 ]
Special Issue on Collective Decision-Making in Living and Artificial Systems. Guest Editors: Andreagiovanni Reina, Eliseo Ferrante, and Gabriele Valentini
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[1]
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A. Reina, E. Ferrante and G. Valentini.
Collective decision-making in living and artificial systems: editorial.
Swarm Intelligence, 15(1-2):1-6, 2021.
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[2]
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J. Chang, S. Powell, E. J. H. Robinson and M. C. Donaldson-Matasci.
Nest choice in arboreal ants is an emergent consequence of network creation under spatial constraints.
Swarm Intelligence, 15(1-2):7-30, 2021.
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[3]
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P. Mavrodiev and F. Schweitzer.
Enanched or distorted wisdom of crowds? An agent-based model of opinion formation under social influence.
Swarm Intelligence, 15(1-2):31-46, 2021.
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[4]
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A. Franci, A. Bizayaeva, S. Park and N. E. Leonard.
Analysis and control of agreement and disagreement opinion cascades.
Swarm Intelligence, 15(1-2):47-82, 2021.
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[5]
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P. Bartashevic and S. Mostaghim.
Multi-featured collective perception with Evidence Theory: tackling spatial correlations.
Swarm Intelligence, 15(1-2):83-110, 2021.
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[6]
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C. Lee, J. Lawry and A. F. T. Winfield.
Negative updating applied to the best-of-n problem with noisy qualities.
Swarm Intelligence, 15(1-2):111-143, 2021.
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[7]
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M. Crosscombe and J. Lawry.
Collective preference learning in the best-of-n problem.
Swarm Intelligence, 15(1-2):145-170, 2021.
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[8]
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T. P. Pavlic, J. Hanson, G. Valentini, S. I. Walker and S. C. Pratt.
Quorum sensing without deliberation: biological inspiration for externalizing computation to physical spaces in multi-robot systems.
Swarm Intelligence, 15(1-2):171-203, 2021.
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Volume 14 [ bib-vol14 ]
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Number 4 / December 2020 [ bib-vol14-num4 ]
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[1]
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V. G. Santos, A. G. Pires, R. J. Alitappeh, P. A. F. Rezeck, L. C. A. Pimenta, D. G. Macharet and L. Chaimowicz.
Spatial segregative behaviors in robotic swarms using differential potentials.
Swarm Intelligence, 14(4):259-284, 2020.
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[2]
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B. Bassimir, M. Schmitt and R. Wanka.
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves.
Swarm Intelligence, 14(4):285-311, 2020.
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[3]
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D. Proverbio, L. Gallo, B. Passalacqua, M. Destefanis, M. Maggiora and J. Pellegrino.
Assessing the robustness of decentralized gathering: a multi-agent approach on micro-biological systems.
Swarm Intelligence, 14(4):313-331, 2020.
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Number 3 / September 2020 [ bib-vol14-num3 ]
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[1]
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V. A. Kazakova, A. S. Wu and G. R. Sukthankar.
Respecializing swarms by forgetting reinforced thresholds.
Swarm Intelligence, 14(3):171-204, 2020.
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[2]
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M. Moussa and G. Beltrame.
On the robustness of consensus-based behaviors for robot swarms.
Swarm Intelligence, 14(3):205-231, 2020.
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[3]
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A. S. Wu, R. P. Wiegand and R. Pradhan.
Response probability enhances robustness in decentralized threshold-based robotic swarms.
Swarm Intelligence, 14(3):233-258, 2020.
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Number 2 / June 2020 [ bib-vol14-num2 ]
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[1]
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V. Trivedi, P. Varshney and M. Ramteke.
A simplified multi-objective particle swarm optimization algorithm.
Swarm Intelligence, 14(2):83-116, 2020.
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[2]
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A. Mirzaeinia, F. Heppner and M. Hassanalian.
An analytical study on leader and follower switching in V-shaped Canada Goose flocks for energy management purposes.
Swarm Intelligence, 14(2):117-141, 2020.
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[3]
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R. Chen, B. Meyer and J. Garcia.
A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation.
Swarm Intelligence, 14(2):143-170, 2020.
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Number 1 / March 2020 [ bib-vol14-num1 ]
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[1]
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A. Ligot and M. Birattari.
Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms.
Swarm Intelligence, 14(1):1-24, 2020.
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[2]
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M. S. Talamali, T. Bose, M. Haire, J. A. R. Marshall and A. Reina.
Sophisticated collective foraging with minimalist agents: a swarm robotics test.
Swarm Intelligence, 14(1):25-56, 2020.
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[3]
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C. Gabelleri, M. Tognon, D. Sanalitro, L Pallottino and A. Franchi.
A study on force-based collaboration in swarms.
Swarm Intelligence, 14(1):57-82, 2020.
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Volume 13 [ bib-vol13 ]
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Number 3-4 / December 2019 [ bib-vol13-num3-4 ]
Special Issue: ANTS 2018 Special Issue. Guest Editors: Marco Dorigo, Mauro Birattari, Christian Blum, Anders L. Christensen, Andreagiovanni Reina, and Vito Trianni
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[1]
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M. Dorigo, M. Birattari, C. Blum, A. L. Christensen, A. Reina and V. Trianni.
ANTS 2018 special issue: Editorial.
Swarm Intelligence, 13(3-4):169-172, 2019.
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[2]
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C. L. Camacho-Villalón, M. Dorigo and T. Stützle.
The intelligent water drops algorithm: why it cannot be considered a novel algorithm.
Swarm Intelligence, 13(3-4):173-192, 2019.
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[3]
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E. T. Oldewage, A. P. Engelbrecht and C. W. Cleghorn.
Degrees of stochasticity in particle swarm optimization..
Swarm Intelligence, 13(3-4):193-215, 2019.
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[4]
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J. Pratseyo, G. De Masi and E. Ferrante.
Collective decision making in dynamic environments.
Swarm Intelligence, 13(3-4):217-243, 2019.
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[5]
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C. Scheepers, A. P. Engelbrecht and C. W. Cleghorn.
Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis.
Swarm Intelligence, 13(3-4):245-276, 2019.
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[6]
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M. Coppola, J. Guo and E. Gill.
The PageRank algorithm as a method to optimize swarm behavior trhough local analysis.
Swarm Intelligence, 11(3-4):277-319, 2019.
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[7]
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I. Raush, A. Reina, P. Simoens and Y. Khaluf.
Coherent collective behaviour emerging from decentralised balancing of social feedback and noise.
Swarm Intelligence, 13(3-4):321-345, 2019.
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[8]
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P. Zahadat and D. N. Hofstadler.
Toward a theory of collective resource distribution: a study of a dynamic morphogenesis controller.
Swarm Intelligence, 13(3-4):347-380, 2019.
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Number 2 / June 2019 [ bib-vol13-num2 ]
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[1]
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F. Saffre, G. Gianini, H. Hildmann, J. Davies, S. Bullock, E. Damiani and J.−L. Deneubourg.
Long-term memory-induced synchronisation can impair collective performance in congested systems.
Swarm Intelligence, 13(2):95-114, 2019.
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[2]
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M. Siddarth, S. Wilson and M. Egerstedt.
Closed-loop task allocation in robot swarms using inter-robot encounters.
Swarm Intelligence, 13(2):115-143, 2019.
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[3]
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R. Zarrouk, I. E. Bennour and A. Jemai.
A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem.
Swarm Intelligence, 13(2):145-168, 2019.
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Number 1 / March 2019 [ bib-vol13-num1 ]
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[1]
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A. W. Schroeder, B. Trease and A. Arsie.
Balancing robot swarm cost and interference effects by varying robot quantity and size.
Swarm Intelligence, 13(1):1-19, 2019.
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[2]
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G. Li, I. Svogor and G. Beltrame.
Long-term pattern formation and maintenance for battery-powered robots.
Swarm Intelligence, 13(1):21-57, 2019.
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[3]
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M. Coppola, J. Guo, E. Gill and G. C. H. E. de Croon.
Provable self-organizing pattern formation by a swarm of robots with limited knowledge.
Swarm Intelligence, 13(1):59-94, 2019.
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Volume 12 [ bib-vol12 ]
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Number 4 / December 2018 [ bib-vol12-num4 ]
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[1]
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S. Bennati.
On the role of collective sensing and evolution in group formation.
Swarm Intelligence, 12(4):267-282, 2018.
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[2]
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E. Crosato, L. Jiang, V. Lecheval, J. T. Lizier, X. R. Wang, P. Tichit, G. Theraulaz and M. Prokopenko.
Informative and misinformative interactions in a school of fish.
Swarm Intelligence, 12(4):283-305, 2018.
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[3]
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R. Miletitch, M. Dorigo and V. Trianni.
Balancing exploitation of renewable resources by a robot swarm.
Swarm Intelligence, 12(4):307-326, 2018.
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[4]
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I. Jang, H. Shin and A. Tsourdos.
Local information-based control for probabilistic swarm distribution guidance.
Swarm Intelligence, 12(4):327-359, 2018.
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[5]
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A. Šošić, A. M. Zoubir and H. Koeppl.
Correction to: Reinforcement learning in a continuum of agents.
Swarm Intelligence, 12(4):361-361, 2018.
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Number 3 / September 2018 [ bib-vol12-num3 ]
Special Issue on Self-Organised Construction. Guest Editors: Heiko Hamann, Sebastian von Mammen, and Ingo Mauser
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[1]
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K. R. Harrison, A. P. Engelbrecht and B. M. Ombuki-Berman.
Self-adaptive particle swarm optimization: a review and analysis of convergence.
Swarm Intelligence, 12(3):187-226, 2018.
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[2]
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F. Bonnet, A. Gribovskiy, J. Halloy and F. Mondada.
Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor.
Swarm Intelligence, 12(3):227-244, 2018.
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[3]
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G. Valentini, A. Antoun, M. Trabattoni, B. Wiandt, Y. Tamura, E. Hocquard, V. Trianni and M. Dorigo.
Kilogrid: a novel experimental environment for the Kilobot robot.
Swarm Intelligence, 12(3):245-266, 2018.
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Number 2 / June 2018 [ bib-vol12-num2 ]
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[1]
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H. Hamann, S. von. Mammel and I. Mausert.
Special issue on self-organised construction.
Swarm Intelligence, 12(2):97-99, 2018.
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[2]
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F. Saffre, H. Hildmann and J. Deneubourg.
Can individual heterogeneity influence self-organised patterns in the termite nest construction model?
Swarm Intelligence, 12(2):101-110, 2018.
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[3]
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T. Wareham and A. Vardy.
Putting it together: the computational complexity of designing robot controllers and environments for distributed construction.
Swarm Intelligence, 12(2):111-128, 2018.
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[4]
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N. Melenbrink and J. Werfel.
Local force cues for strength and stability in a distributed robotic construction system.
Swarm Intelligence, 12(2):129-153, 2018.
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[5]
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A. Groenewolt, T. Schwinn, L. Nguyen and A. Menges.
An interactive agent-based framework for materialization-informed architectural design.
Swarm Intelligence, 12(2):155-186, 2018.
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Number 1 / March 2018 [ bib-vol12-num1 ]
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[1]
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C. W. Cleghorn and A. P. Engelbrecht.
Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption.
Swarm Intelligence, 12(1):1-22, 2018.
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[2]
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A. Šošić, A. M. Zoubir and H. Koeppl.
Reinforcement learning in a continuum of agents.
Swarm Intelligence, 12(1):23-51, 2018.
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[3]
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A. Kasprzok, B. Ayalew and C. Lau.
An ant-inspired model for multi-agent interaction networks without stigmergy.
Swarm Intelligence, 12(1):53-69, 2018.
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[4]
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L. Pitonakova, R. Crowder and S. Bullock.
The Information-Cost-Reward framework for understanding robot swarm foraging.
Swarm Intelligence, 12(1):71-96, 2018.
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Volume 11 [ bib-vol11 ]
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Number 3-4 / December 2017 [ bib-vol11-num3-4 ]
Special Issue: ANTS 2016 Special Issue. Guest Editors: Marco Dorigo, Mauro Birattari, Xiaodong Li, Manuel López-Ibáñez, Katsunari Ohkura, Carlo Pinciroli, and Thomas Stützle
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[1]
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M. Dorigo, M. Birattari, X. Li, M. López-Ibáñez, K. Ohkura, C. Pinciroli and T. Stützle.
ANTS 2016 special issue: Editorial.
Swarm Intelligence, 11(3-4):181-183, 2017.
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[2]
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M. H. M. Alkilabi, A. Narayan and E. Tuci.
Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies.
Swarm Intelligence, 11(3-4):185-209, 2017.
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[3]
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K. M. Salama and A. M. Abdelbar.
Learning cluster-based classification systems with ant colony optimization algorithms.
Swarm Intelligence, 11(3-4):211-242, 2017.
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[4]
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B. Haghighat and A. Martinoli.
Automatic synthesis of rulesets for programmable stochastic self-assembly of rotationally symmetric robotic modules.
Swarm Intelligence, 11(3-4):243-270, 2017.
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[5]
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L. I. Bellaiche and A. Bruckstein.
Continuous time gathering of agents with limited visibility and bearing-only sensing.
Swarm Intelligence, 11(3-4):271-293, 2017.
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[6]
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A. Erskine, T. Joyce and J. M. Herrman.
Stochastic stability of particle swarm optimisation.
Swarm Intelligence, 11(3-4):295-315, 2017.
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[7]
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M. Goodwin, T. Tufteland, G. Ødesneltvedt and A. Yazidi.
PolyACO+: a multi-level polygon-based ant colony optimisation classifier.
Swarm Intelligence, 11(3-4):317-346, 2017.
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Number 2 / June 2017 [ bib-vol11-num2 ]
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[1]
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O. R. Castro Jr., A. Pozo, J. A. Lozano and R. Santana.
An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study.
Swarm Intelligence, 11(2):101-130, 2017.
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[2]
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B. Meyer.
Optimal information transfer and stochastic resonance in collective decision making.
Swarm Intelligence, 11(2):131-154, 2017.
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[3]
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Y. Khaluf, C. Pinciroli, G. Valentini and H. Hamann.
The impact of agent density on scalability in collective systems: noise-induced versus majority-based bistability.
Swarm Intelligence, 11(2):155-179, 2017.
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Number 1 / March 2017 [ bib-vol11-num1 ]
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[1]
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D. Aydın, G. Yavuz and T. Stützle.
ABC-X: a generalized, automatically configurable artificial bee colony framework.
Swarm Intelligence, 11(1):1-38, 2017.
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[2]
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A. Schroeder, S. Ramakrishnan, K. Manish and B. Trease.
Efficient spatial coverage by a robot swarm based on an ant foraging model and the Lévy distribution.
Swarm Intelligence, 11(1):39-69, 2017.
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[3]
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J. S. Falcón-Cardona and C. A. Coello Coello.
A new indicator-based many-objective ant colony optimizer for continuous search spaces.
Swarm Intelligence, 11(1):71-100, 2017.
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Volume 10 [ bib-vol10 ]
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Number 4 / December 2016 [ bib-vol10-num4 ]
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[1]
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M. Dorigo.
Editorial: Ten years of swarm intelligence.
Swarm Intelligence, 10(4):245-246, 2016.
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[2]
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L. Mondada, M. E. Karim and F. Mondada.
Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms.
Swarm Intelligence, 10(4):247-265, 2016.
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[3]
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K. R. Harrison, A. P. Engelbrecht and B. M. Ombuki-Berman.
Inertia weight control strategies for particle swarm optimization.
Swarm Intelligence, 10(4):267-305, 2016.
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[4]
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A. P. Piotrowski and J. J. Napiorkowski.
Searching for structural bias in particle swarm optimization and differential evolution algorithms.
Swarm Intelligence, 10(4):307-353, 2016.
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Number 3 / September 2016 [ bib-vol10-num3 ]
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[1]
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S. Taghiyeh and J. Xu.
Turing learning: a metric-free approach to inferring behavior and its application to swarms.
Swarm Intelligence, 10(3):161-192, 2016.
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[2]
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G. Podevijn, R. O'Grady, N. Mathews, A. Gilles, C. Fantini-Hauwel and M. Dorigo.
Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human--swarm interaction.
Swarm Intelligence, 10(3):193-210, 2016.
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[3]
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W. Li, M. Gauci and R. Groß.
Turing learning: a metric-free approach to inferring behavior and its application to swarms.
Swarm Intelligence, 10(3):211-243, 2016.
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Number 2 / June 2016 [ bib-vol10-num2 ]
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[1]
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E. Ampellio and L. Vassio.
A hybrid swarm-based algorithm for single-objective optimization
problems involving high-cost analyses
Swarm Intelligence, 10(2):99-121, 2016.
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[2]
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H. D. Menéndez, F. E. B. Otero, and D. Camacho.
Medoid-based clustering using ant colony optimization.
Swarm Intelligence, 10(2):123-145, 2016.
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[3]
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A. Kanakia, B. Touri, and N. Correll.
Modeling multi-robot task allocation with limited information as global game.
Swarm Intelligence, 10(2):147-160, 2016.
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Number 1 / March 2016 [ bib-vol10-num1 ]
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[1]
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E. Castello, T. Yamamoto, F. Dalla Libera, W. Liu, A. F. T. Winfield,
Y. Nakamura, and H. Ishiguro.
Adaptive foraging for simulated and real robotic swarms: the
dynamical response threshold approach.
Swarm Intelligence, 10(1):1-31, 2016.
[ bib ]
|
|
[2]
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L. Pitonakova, R. Crowder, and S. Bullock.
Information flow principles for plasticity in foraging robot swarms.
Swarm Intelligence, 10(1):33-63, 2016.
[ bib ]
|
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[3]
|
Y. K. Lopes, S. M. Trenkwalder, A. B. Leal, T. J. Dodd, and R. Groß.
Supervisory control theory applied to swarm robotics.
Swarm Intelligence, 10(1):65-97, 2016.
[ bib ]
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|
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Volume 9 [ bib-vol9 ]
|
Number 4 / December 2015 [ bib-vol9-num4 ]
|
[1]
|
K. M. Salama and A. M. Abdelbar.
Learning neural network structures with ant colony algorithms.
Swarm Intelligence, 9(4):229-265, 2015.
[ bib ]
|
|
[2]
|
E. Tuci and A. Rabérin.
On the design of generalist strategies for swarms of simulated robots
engaged in a task-allocation scenario.
Swarm Intelligence, 9(4):267-290, 2015.
[ bib ]
|
|
[3]
|
B. J. Leonard, A. P. Engelbrecht, and C. W. Cleghorn.
Critical considerations on angle modulated particle swarm optimisers.
Swarm Intelligence, 9(4):291-314, 2015.
[ bib ]
|
|
[4]
|
J. Albinati, S. E. L. Oliveira, F. E. B. Otero, and G. L. Pappa.
An ant colony-based semi-supervised approach for learning
classification rules.
Swarm Intelligence, 9(4):315-341, 2015.
[ bib ]
|
|
|
Number 2-3 / September 2015 [ bib-vol9-num2-3 ]
Special Issue: ANTS 2014 Special Issue. Guest Editors: Marco Dorigo, Mauro Birattari, Simon Garnier, Heiko Hamann, Marco A. Montes de Oca, Christine Solnon, and Thomas Stützle
|
[1]
|
M. Dorigo, M. Birattari, S. Garnier, H. Hamann, M. A. Montes de Oca,
C. Solnon, and T. Stützle.
ANTS 2014 special issue: Editorial.
Swarm Intelligence, 9(2-3):71-73, 2015.
[ bib ]
|
|
[2]
|
A. Reina, R. Miletitch, M. Dorigo, and V. Trianni.
A quantitative micro–macro link for collective decisions: the
shortest path discovery/selection example.
Swarm Intelligence, 9(2-3):75-102, 2015.
[ bib ]
|
|
[3]
|
L. Pérez Cáceres, M. López-Ibáñez, and T. Stützle.
Ant colony optimization on a limited budget of evaluations.
Swarm Intelligence, 9(2-3):103-124, 2015.
[ bib ]
|
|
[4]
|
G. Francesca, M. Brambilla, A. Brutschy, L. Garattoni, R. Miletitch,
G. Podevijn, A. Reina, T. Soleymani, M. Salvaro, C. Pinciroli, F. Mascia,
V. Trianni, and M. Birattari.
AutoMoDe-Chocolate: automatic design of control software for robot
swarms.
Swarm Intelligence, 9(2-3):125-152, 2015.
[ bib ]
|
|
[5]
|
G. Valentini and H. Hamann.
Time-variant feedback processes in collective decision-making
systems: influence and effect of dynamic neighborhood sizes.
Swarm Intelligence, 9(2-3):153-176, 2015.
[ bib ]
|
|
[6]
|
A. P. Engelbrecht C. W. Cleghorn.
Particle swarm variants: standardized convergence analysis.
Swarm Intelligence, 9(2-3):177-203, 2015.
[ bib ]
|
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[7]
|
C. Blum, B. Calvo, and M. J. Blesa.
FrogCOL and FrogMIS: new decentralized algorithms for finding large
independent sets in graphs.
Swarm Intelligence, 9(2-3):205-227, 2015.
[ bib ]
|
|
|
Number 1 / March 2015 [ bib-vol9-num1 ]
|
[1]
|
A. Brutschy, L. Garattoni, M. Brambilla, G. Francesca, G. Pini, M. Dorigo, and
Mauro Birattari.
The TAM: abstracting complex tasks in swarm robotics research.
Swarm Intelligence, 9(1):1-22, 2015.
[ bib ]
|
|
[2]
|
J. Harvey, K. Merrick, and H. A. Abbass.
Application of chaos measures to a simplified boids flocking model.
Swarm Intelligence, 9(1):23-41, 2015.
[ bib ]
|
|
[3]
|
J. P. Hecker and M. E. Moses.
Beyond pheromones: evolving error-tolerant, flexible, and scalable
ant-inspired robot swarms.
Swarm Intelligence, 9(1):43-70, 2015.
[ bib ]
|
|
|
Volume 8 [ bib-vol8 ]
|
Number 4 / December 2014 [ bib-vol8-num4 ]
|
[1]
|
J. Rada-Vilela, M. Johnston, and M. Zhang.
Deception, blindness and disorientation in particle swarm
optimization applied to noisy problems.
Swarm Intelligence, 8(4):247-273, 2014.
[ bib ]
|
|
[2]
|
K. M. Malan and A. P. Engelbrecht.
Characterising the searchability of continuous optimisation problems
for PSO.
Swarm Intelligence, 8(4):275-302, 2014.
[ bib ]
|
|
[3]
|
S. Wilson, T. P. Pavlic, G. P. Kumar, A. Buffin, S. C. Pratt, and S. Berman.
Design of ant-inspired stochastic control policies for collective
transport by robotic swarms.
Swarm Intelligence, 8(4):303-327, 2014.
[ bib ]
|
|
[4]
|
G. Sartoretti, M. Hongler, M. Elias de Oliveira, and F. Mondada.
Decentralized self-selection of swarm trajectories: from dynamical
systems theory to robotic implementation.
Swarm Intelligence, 8(4):329-351, 2014.
[ bib ]
|
|
|
Number 3 / September 2014 [ bib-vol8-num3 ]
|
[1]
|
M. R. Bonyadi and Z. Michalewicz.
A locally convergent rotationally invariant particle swarm
optimization algorithm.
Swarm Intelligence, 8(3):159-198, 2014.
[ bib ]
|
|
[2]
|
A. Abdul Khaliq, M. Di Rocco, and A. Saffiotti.
Stigmergic algorithms for multiple minimalistic robots on an RFID
floor.
Swarm Intelligence, 8(3):199-225, 2014.
[ bib ]
|
|
[3]
|
R. Fujisawa, S. Dobata, K. Sugawara, and F. Matsuno.
Designing pheromone communication in swarm robotics: Group foraging
behavior mediated by chemical substance.
Swarm Intelligence, 8(3):227-246, 2014.
[ bib ]
|
|
|
Number 2 / June 2014 [ bib-vol8-num2 ]
|
[1]
|
G. Francesca, M. Brambilla, A. Brutschy, V. Trianni, and M. Birattari.
AutoMoDe: A novel approach to the automatic design of control
software for robot swarms.
Swarm Intelligence, 8(2):89-112, 2014.
[ bib ]
|
|
[2]
|
O. Simonin, F. Charpillet, and E. Thierry.
Revisiting wavefront construction with collective agents: an approach
to foraging.
Swarm Intelligence, 8(2):113-138, 2014.
[ bib ]
|
|
[3]
|
C. L. Simons, J. Smith, and P. White.
Interactive ant colony optimization (iACO) for early lifecycle
software design.
Swarm Intelligence, 8(2):139-157, 2014.
[ bib ]
|
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|
Number 1 / March 2014 [ bib-vol8-num1 ]
|
[1]
|
F. Ducatelle, G. A. Di Caro, A. Förster, M. Bonani, M. Dorigo, S. Magnenat,
F. Mondada, R. O'Grady, P. Rétornaz C. Pinciroli, V. Trianni, and L. M.
Gambardella.
Cooperative navigation in robotic swarms.
Swarm Intelligence, 8(1):1-33, 2014.
[ bib ]
|
|
[2]
|
C. W. Cleghorn and A. P. Engelbrecht.
A generalized theoretical deterministic particle swarm model.
Swarm Intelligence, 8(1):35-59, 2014.
[ bib ]
|
|
[3]
|
A. Vardy, G. Vorobyev, and W. Banzhaf.
Cache consensus: rapid object sorting by a robotic swarm.
Swarm Intelligence, 8(1):61-87, 2014.
[ bib ]
|
|
|
Volume 7 [ bib-vol7 ]
|
Number 4 / December 2013 [ bib-vol7-num4 ]
|
[1]
|
M. Matteucci and L. Mussone.
An ant colony system for transportation user equilibrium analysis in
congested networks.
Swarm Intelligence, 7(4):255-277, 2013.
[ bib ]
|
|
[2]
|
R. C. Fetecau and J. Meskas.
A nonlocal kinetic model for predator–prey interactions.
Swarm Intelligence, 7(4):279-305, 2013.
[ bib ]
|
|
[3]
|
H. Sasaki and H. Leung.
Trail traffic flow prediction by contact frequency among individual
ants.
Swarm Intelligence, 7(4):307-326, 2013.
[ bib ]
|
|
[4]
|
T. Liao, D. Aydın, and T. Stützle.
Artificial bee colonies for continuous optimization: Experimental
analysis and improvements.
Swarm Intelligence, 7(4):327-356, 2013.
[ bib ]
|
|
|
Numbers 2-3 / September 2013 [ bib-vol7-num2-3 ]
Special Issue: ANTS 2012 Special Issue. Guest Editors: Marco Dorigo, Mauro Birattari, Christian Blum, Anders Lyhne Christensen, Andries Engelbrecht, Roderich Groß, and Thomas Stützle
|
[1]
|
M. Dorigo, M. Birattari, C. Blum, A. L. Christensen, A. Engelbrecht,
R. Groß, and T. Stützle.
ANTS 2012 special issue.
Swarm Intelligence, 7(2-4):79-81, 2013.
[ bib ]
|
|
[2]
|
L. Murray, J. Timmis, and A. Tyrrell.
Modular self-assembling and self-reconfiguring e-pucks.
Swarm Intelligence, 7(2-4):83-113, 2013.
[ bib ]
|
|
[3]
|
J. Gomes, P. Urbano, and A. L. Christensen.
Evolution of swarm robotics systems with novelty search.
Swarm Intelligence, 7(2-4):115-144, 2013.
[ bib ]
|
|
[4]
|
H. Hamann.
Towards swarm calculus: urn models of collective decisions and
universal properties of swarm performance.
Swarm Intelligence, 7(2-4):145-172, 2013.
[ bib ]
|
|
[5]
|
G. Pini, M. Gagliolo, A. Brutschy, M. Dorigo, and M. Birattari.
Task partitioning in a robot swarm: a study on the effect of
communication.
Swarm Intelligence, 7(2-4):173-199, 2013.
[ bib ]
|
|
[6]
|
M. Massink, M. Brambilla, D. Latella, and M. Birattari M. Dorigo and.
On the use of Bio-PEPA for modelling and analysing collective
behaviours in swarm robotics.
Swarm Intelligence, 7(2-4):201-228, 2013.
[ bib ]
|
|
[7]
|
K. M. Salama and A. A. Freitas.
Learning Bayesian network classifiers using ant colony
optimization.
Swarm Intelligence, 7(2-4):229-254, 2013.
[ bib ]
|
|
|
Number 1 / March 2013 [ bib-vol7-num1 ]
|
[1]
|
M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo.
Swarm robotics: a review from the swarm engineering perspective.
Swarm Intelligence, 7(1):1-41, 2013.
[ bib ]
|
|
[2]
|
Jayadeva, S. Shah, A. Bhaya, R. Kothari, and S. Chandra.
Ants find the shortest path: a mathematical proof.
Swarm Intelligence, 7(1):43-62, 2013.
[ bib ]
|
|
[3]
|
D. D. Do, S. V. Le, and X. H. Hoang.
ACOHAP: an efficient ant colony optimization for the haplotype
inference by pure parsimony problem.
Swarm Intelligence, 7(1):63-77, 2013.
[ bib ]
|
|
|
Volume 6 [ bib-vol6 ]
|
Number 4 / December 2012 [ bib-vol6-num4 ]
|
[1]
|
C. Pinciroli, V. Trianni, R. O'Grady, G. Pini, A. Brutschy, M. Brambilla,
N. Mathews, E. Ferrante, G. Di Caro, F. Ducatelle, M. Birattari, L. M.
Gambardella, and M. Dorigo.
ARGoS: A modular, parallel, multi-engine simulator for multi-robot
systems.
Swarm Intelligence, 6(4):271-295, 2012.
[ bib ]
|
|
[2]
|
J. Langeveld and A. P. Engelbrecht.
Set-based particle swarm optimization applied to the multidimensional
knapsack problem.
Swarm Intelligence, 6(4):297-342, 2012.
[ bib ]
|
|
[3]
|
C. Iacopino and P. Palmer.
The dynamics of ant colony optimization algorithms applied to binary
chains.
Swarm Intelligence, 6(4):343-377, 2012.
[ bib ]
|
|
|
Number 3 / September 2012 [ bib-vol6-num3 ]
|
[1]
|
A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh.
A competitive clustering particle swarm optimizer for dynamic
optimization problems.
Swarm Intelligence, 6(3):177-206, 2012.
[ bib ]
|
|
[2]
|
M. López-Ibáñez and T. Stützle.
An experimental analysis of design choices of multi-objective ant
colony optimization algorithms.
Swarm Intelligence, 6(3):207-232, 2012.
[ bib ]
|
|
[3]
|
A. S. Rakitianskaia and A. P. Engelbrecht.
Training feedforward neural networks with dynamic particle swarm
optimisation.
Swarm Intelligence, 6(3):233-270, 2012.
[ bib ]
|
|
|
Number 2 / June 2012 [ bib-vol6-num2 ]
|
[1]
|
M. Gardner, A. McNabb, and K. Seppi.
A speculative approach to parallelization in particle swarm
optimization.
Swarm Intelligence, 6(2):77-116, 2012.
[ bib ]
|
|
[2]
|
H. Hernández and C. Blum.
Distributed graph coloring: an approach based on the calling behavior
of Japanese tree frogs.
Swarm Intelligence, 6(2):117-150, 2012.
[ bib ]
|
|
[3]
|
R.M. Brito, T. M. Schaerf, M. R. Myerscough, T. A. Heard, and B. P. Oldroyd.
Brood comb construction by the stingless bees Tetragonula hockingsi
and Tetragonula carbonarias.
Swarm Intelligence, 6(2):151-176, 2012.
[ bib ]
|
|
|
Number 1 / March 2012 [ bib-vol6-num1 ]
Special Issue: ANTS 2010 Special Issue, Part 2. Guest Editors: Marco Dorigo, Mauro Birattari, Gianni A. Di Caro, René Doursat, Andries Engelbrecht, Luca Maria Gambardella, Roderich Groß, Erol Şahin and Thomas Stützle
|
[1]
|
T. Kötzing, F. Neumann, H. Röglin, and C. Witt.
Theoretical analysis of two ACO approaches for the traveling
salesman problem.
Swarm Intelligence, 6(1):1-21, 2012.
[ bib ]
|
|
[2]
|
P. Pellegrini, T. Stützle, and M. Birattari.
A critical analysis of parameter adaptation in ant colony
optimization.
Swarm Intelligence, 6(1):23-48, 2012.
[ bib ]
|
|
[3]
|
Z. Yuan, M. A. Montes de Oca, M. Birattari, and T. Stützle.
Continuous optimization algorithms for tuning real and integer
parameters of swarm intelligence algorithms.
Swarm Intelligence, 6(1):49-75, 2012.
[ bib ]
|
|
|
Volume 5 [ bib-vol5 ]
|
Numbers 3-4 / December 2011 [ bib-vol5-num3-4 ]
Special Issue: ANTS 2010 Special Issue, Part 1. Guest Editors: Marco Dorigo, Mauro Birattari, Gianni A. Di Caro, René Doursat, Andries Engelbrecht, Luca Maria Gambardella, Roderich Groß, Erol Şahin and Thomas Stützle
|
[1]
|
M. Dorigo, M. Birattari, G. Di Caro, R. Doursat, A. Engelbrecht, L. M.
Gambardella, R. Groß, E. Sahin, and T. Stützle.
ANTS 2010 special issue editorial.
Swarm Intelligence, 5(3-4):143-147, 2011.
[ bib ]
|
|
[2]
|
K. M. Salama, A. M. Abdelbar, and A. A. Freitas.
Multiple pheromone types and other extensions to the Ant-Miner
classification rule discovery algorithm.
Swarm Intelligence, 5(3-4):149-182, 2011.
[ bib ]
|
|
[3]
|
K. Li, C. E. Torres, K. Thomas, L. F. Rossi, and C.-C. Shen.
Slime mold inspired routing protocols for wireless sensor networks.
Swarm Intelligence, 5(3-4):183-223, 2011.
[ bib ]
|
|
[4]
|
L. Yamamoto, D. Miorandi, P. Collet, and W. Banzhaf.
Recovery properties of distributed cluster head election using
reaction–diffusion.
Swarm Intelligence, 5(3-4):225-255, 2011.
[ bib ]
|
|
[5]
|
J. Beal.
Functional blueprints: an approach to modularity in grown systems.
Swarm Intelligence, 5(3-4):257-281, 2011.
[ bib ]
|
|
[6]
|
G. Pini, A. Brutschy, M. Frison, A. Roli, M. Dorigo, and M. Birattari.
Task partitioning in swarms of robots: an adaptive method for
strategy selection.
Swarm Intelligence, 5(3-4):283-304, 2011.
[ bib ]
|
|
[7]
|
M. A. Montes de Oca, E. Ferrante, A. Scheidler, C. Pinciroli, M. Birattari,
and M. Dorigo.
Majority-rule opinion dynamics with differential latency: a mechanism
for self-organized collective decision-making.
Swarm Intelligence, 5(3-4):305-327, 2011.
[ bib ]
|
|
|
Number 2 / June, 2011 [ bib-vol5-num2 ]
|
[1]
|
F. Ducatelle, C. Pinciroli G.A. Di Caro, and L. M. Gambardella.
Self-organized cooperation between robotic swarms.
Swarm Intelligence, 5(2):73-96, 2011.
[ bib ]
|
|
[2]
|
V. Sperati, V. Trianni, and S. Nolfi.
Self-organised path formation in a swarm of robots.
Swarm Intelligence, 5(2):97-119, 2011.
[ bib ]
|
|
[3]
|
K.Diwold, T.M. Schaerf, M.R. Myerscough, M. Middendorf, and M. Beekman.
Deciding on the wing: in-flight decision making and search space
sampling in the red dwarf honeybee Apis florea.
Swarm Intelligence, 5(2):121-141, 2011.
[ bib ]
|
|
|
Number 1 / January, 2011 [ bib-vol5-num1 ]
Special Issue: Swarm Cognition. Guest Editors: Vito Trianni, Elio Tuci and Kevin M. Passino
|
[1]
|
V.Trianni, E. Tuci, and K. M. Passino.
Special issue on swarm cognition.
Swarm Intelligence, 5(1):1-2, 2011.
[ bib ]
|
|
[2]
|
V.Trianni, E. Tuci, K. M. Passino, and J. A. R. Marshall.
Swarm cognition: an interdisciplinary approach to the study of
self-organising biological collectives.
Swarm Intelligence, 5(1):3-18, 2011.
[ bib ]
|
|
[3]
|
J. S.Turner.
Termites as models of swarm cognition.
Swarm Intelligence, 5(1):19-43, 2011.
[ bib ]
|
|
[4]
|
P. Santana and L. Correia.
Swarm cognition on off-road autonomous robots.
Swarm Intelligence, 5(1):45-72, 2011.
[ bib ]
|
|
|
Volume 4 [ bib-vol4 ]
|
Number 4 / November, 2010 [ bib-vol4-num4 ]
Special Issue: Artificial Immune Systems. Guest Editors: Jon Timmis, Paul S. Andrews and Emma Hart
|
[1]
|
J. Timmis, P. Andrews, and E. Hart.
Special issue on artificial immune systems.
Swarm Intelligence, 4(4):245-246, 2010.
[ bib ]
|
|
[2]
|
J. Timmis, P. Andrews, and E. Hart.
On artificial immune systems and swarm intelligence.
Swarm Intelligence, 4(4):247-273, 2010.
[ bib ]
|
|
[3]
|
N. Nanas, M. Vavalis, and A. De Roeck.
Words, antibodies and their interactions.
Swarm Intelligence, 4(4):275-300, 2010.
[ bib ]
|
|
[4]
|
S. Banerjee and M. Moses.
Scale invariance of immune system response rates and times:
perspectives on immune system architecture and implications for artificial
immune systems.
Swarm Intelligence, 4(4):301-318, 2010.
[ bib ]
|
|
[5]
|
A. Sorathiya, A. Bracciali, and P. Liò.
An integrated modelling approach for R5–X4 mutation and HAART
therapy assessment.
Swarm Intelligence, 4(4):319-340, 2010.
[ bib ]
|
|
Number 3 / September, 2010 [ bib-vol4-num3 ]
|
[1]
|
F. Ducatelle, G.A. Di Caro, and L.M. Gambardella.
Principles and applications of swarm intelligence for adaptive
routing in telecommunications networks.
Swarm Intelligence, 4(3):173-198, 2010.
[ bib ]
|
|
[2]
|
C. A.C. Parker and H. Zhang.
Collective unary decision-making by decentralized multiple-robot
systems applied to the task-sequencing problem.
Swarm Intelligence, 4(3):199-220, 2010.
[ bib ]
|
|
[3]
|
C.E. Torres, L. F. Rossi, J. Keffer, K. Li, and C.-C. Shen.
Modeling, analysis and simulation of ant-based network routing
protocols.
Swarm Intelligence, 4(3):221-244, 2010.
[ bib ]
|
|
Number 2 / June, 2010 [ bib-vol4-num2 ]
|
[1]
|
N. Fatès.
Solving the decentralised gathering problem with a
reaction–diffusion–chemotaxis scheme.
Swarm Intelligence, 4(2):91-115, 2010.
[ bib ]
|
|
[2]
|
T. Stirling, S. Wischmann, and D. Floreano.
Energy-efficient indoor search by swarms of simulated flying robots
without global information.
Swarm Intelligence, 4(2):117-143, 2010.
[ bib ]
|
|
[3]
|
S. Shah, R. Kothari, Jayadeva, and S. Chandra.
Trail formation in ants. a generalized polya urn process.
Swarm Intelligence, 4(2):145-171, 2010.
[ bib ]
|
|
Number 1 / March, 2010 [ bib-vol4-num1 ]
|
[1]
|
C. E. Martin and J. A. Reggia.
Self-assembly of neural networks viewed as swarm intelligence.
Swarm Intelligence, 4(1):1-36, 2010.
[ bib ]
|
|
[2]
|
G. Antonelli, F. Arrichiello, and S. Chiaverini.
Flocking for multi-robot systems via the Null-space-based
Behavioral control.
Swarm Intelligence, 4(1):37-56, 2010.
[ bib ]
|
|
[3]
|
M. El-Abd and M. S. Kamel.
A cooperative particle swarm optimizer with migration of
heterogeneous probabilistic models.
Swarm Intelligence, 4(1):57-89, 2010.
[ bib ]
|
|
|
Volume 3 [ bib-vol3 ]
|
Number 4 / December, 2009 [ bib-vol3-num4 ]
Special Issue: Particle Swarm Optimization. Guest Editors: Riccardo Poli, Andries Engelbrecht, and Jim Kennedy
|
[1]
|
R. Poli, A. Engelbrecht, and J. Kennedy.
Editorial for the special issue on particle swarm optimization.
Swarm Intelligence, 3(4):243-244, 2009.
[ bib ]
|
|
[2]
|
J. L. Fernández Martínez and E. García Gonzalo.
The PSO family: deduction, stochastic analysis and comparison.
Swarm Intelligence, 3(4):245-273, 2009.
[ bib ]
|
|
[3]
|
Y. Yan and L. A. Osadciw.
Density estimation using a new dimension adaptive particle swarm
optimization algorithm.
Swarm Intelligence, 3(4):275-301, 2009.
[ bib ]
|
|
[4]
|
B. Samanta and C. Nataraj.
Application of particle swarm optimization and proximal support
vector machines for fault detection.
Swarm Intelligence, 3(4):303-325, 2009.
[ bib ]
|
|
Number 3 / September, 2009 [ bib-vol3-num3 ]
|
[1]
|
M. Schilde, K. F. Doerner, R. F. Hartl, and G. Kiechle.
Metaheuristics for the bi-objective orienteering problem.
Swarm Intelligence, 3(3):179-201, 2009.
[ bib ]
|
|
[2]
|
J. Pugh and A. Martinoli.
Distributed scalable multi-robot learning using particle swarm
optimization.
Swarm Intelligence, 3(3):203-222, 2009.
[ bib ]
|
|
[3]
|
P. Balaprakash, M. Birattari, T. Stützle, Z. Yuan, and M. Dorigo.
Estimation-based ant colony optimization and local search for the
probabilistic traveling salesman problem.
Swarm Intelligence, 3(3):223-242, 2009.
[ bib ]
|
|
Number 2 / June, 2009 [ bib-vol3-num2 ]
|
[1]
|
K. N. Krishnanand and D. Ghose.
Glowworm swarm optimization for simultaneous capture of multiple
local optima of multimodal functions.
Swarm Intelligence, 3(2):87-124, 2009.
[ bib ]
|
|
[2]
|
H. Hernández and C.Blum.
Ant colony optimization for multicasting in static wireless ad-hoc
networks.
Swarm Intelligence, 3(2):125-148, 2009.
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[3]
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Y. Cooren, M. Clerc, and P. Siarry.
Performance evaluation of TRIBES, an adaptive particle swarm
optimization algorithm.
Swarm Intelligence, 3(2):149-178, 2009.
[ bib ]
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Number 1 / March, 2009 [ bib-vol3-num1 ]
Special Issue: Ant Colony Optimization. Guest Editors: Karl Doerner, Daniel Merkle and Thomas Stützle
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[1]
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K. F. Doerner, D. Merkle, and T. Stützle.
Special issue on ant colony optimization.
Swarm Intelligence, 3(1):1-2, 2009.
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[2]
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V. S. Borkar and D. Das.
A novel ACO algorithm for optimization via reinforcement and
initial bias.
Swarm Intelligence, 3(1):3-34, 2009.
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[3]
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F. Neumann, D. Sudholt, and C. Witt.
Analysis of different MMAS ACO algorithms on unimodal functions and
plateaus.
Swarm Intelligence, 3(1):35-68, 2009.
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[4]
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D. Angus and C. Woodward.
Multiple objective ant colony optimisation.
Swarm Intelligence, 3(1):69-85, 2009.
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Volume 2 [ bib-vol2 ]
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Numbers 2-4 / December, 2008 [ bib-vol2-num2-4 ]
Special Issue: Swarm Robotics. Guest Editors: Erol Şahin and Alan Winfield
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[1]
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E. Şahin and A. Winfield.
Special issue on swarm robotics.
Swarm Intelligence, 2(2-4):69-72, 2008.
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[2]
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V. Sperati, V. Trianni, and S. Nolfi.
Evolving coordinated group behaviours through maximisation of mean
mutual information.
Swarm Intelligence, 2(2-4):73-95, 2008.
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[3]
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A. E. Turgut, H. Çelikkanat, F. Gökçe, and E. Şahin.
Self-organized flocking in mobile robot swarms.
Swarm Intelligence, 2(2-4):97-120, 2008.
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[4]
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M. A. Hsieh, A. Halasz, S. Berman, and V. Kumar.
Biologically inspired redistribution of a swarm of robots among
multiple sites.
Swarm Intelligence, 2(2-4):121-141, 2008.
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[5]
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A. L. Christensen, R. O'Grady, and M. Dorigo.
SWARMORPH-script: a language for arbitrary morphology generation in
self-assembling robots.
Swarm Intelligence, 2(2-4):143-165, 2008.
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[6]
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S. Hauert, L. Winkler, J.-C. Zufferey, and D. Floreano.
Ant-based swarming with positionless micro air vehicles for
communication relay.
Swarm Intelligence, 2(2-4):167-188, 2008.
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[7]
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R. Vaughan.
Massively multi-robot simulation in stage.
Swarm Intelligence, 2(2-4):189-208, 2008.
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[8]
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H. Hamann and H. Wörn.
A framework of space-time continuous models for algorithm design in
swarm robotics.
Swarm Intelligence, 2(2-4):209-239, 2008.
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[9]
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A. F. T. Winfield, W. Liu, J. Nembrini, and A. Martinoli.
Modelling a wireless connected swarm of mobile robots.
Swarm Intelligence, 2(2-4):241-266, 2008.
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Number 1 / March, 2008 [ bib-vol2-num1 ]
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[1]
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S. Nouyan, A. Campo, and M. Dorigo.
Path formation in a robot swarm: Self-organized strategies to find
your way home.
Swarm Intelligence, 2(1):1-23, 2008.
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[2]
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A. John, A. Schadschneider, D. Chowdhury, and K. Nishinari.
Characteristics of ant-inspired traffic flow.
Swarm Intelligence, 2(1):25-41, 2008.
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[3]
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G. L. Peterson, C. B. Mayer, and T. L. Kubler.
Ant clustering with locally weighted ant perception and diversified
memory.
Swarm Intelligence, 2(1):43-68, 2008.
[ bib ]
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Volume 1 [ bib-vol1 ]
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Number 2 / December, 2007 [ bib-vol1-num2 ]
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[1]
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N. R. Franks, J. W. Hooper, M. Gumn, T. H. Bridger, J. A. R. Marshall,
R. Gross, and A. Dornhaus.
Moving targets: collective decisions and flexible choices in
house-hunting ants.
Swarm Intelligence, 1(2):81-94, 2007.
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[2]
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J. Handl and B. Meyer.
Ant-based and swarm-based clustering.
Swarm Intelligence, 1(2):95-113, 2007.
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[3]
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O. Korb, T. Stützle, and T. E. Exner.
An ant colony optimization approach to flexible protein-ligand
docking.
Swarm Intelligence, 1(2):115-134, 2007.
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[4]
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A.E. Rizzoli, R. Montemanni, E. Lucibello, and L.M. Gambardella.
Ant colony optimization for real-world vehicle routing problems.
Swarm Intelligence, 1(2):135-151, 2007.
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Number 1 / June, 2007 [ bib-vol1-num2 ]
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[1]
|
M. Dorigo.
Editorial.
Swarm Intelligence, 1(1):1-2, 2007.
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[2]
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S. Garnier, J. Gautrais, and G. Theraulaz.
The biological principles of swarm intelligence.
Swarm Intelligence, 1(1):3-31, 2007.
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[3]
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R. Poli, J. Kennedy, and T. Blackwell.
Particle swarm optimization.
Swarm Intelligence, 1(1):33-57, 2007.
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[4]
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W. Gutjahr.
Mathematical runtime analysis of ACO algorithms: Survey on an
emerging issue.
Swarm Intelligence, 1(1):59-79, 2007.
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