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Modeling and optimization of adaptive foraging in swarm robotic systems

Liu, Wenguo; Winfield, Alan F.T.

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Wenguo Liu


Understanding the effect of individual parameters on the collective performance of swarm robotic systems in order to design and optimize individual robot behaviors is a significant challenge. In this paper we present a macroscopic probabilistic model of adaptive collective foraging in a swarm of robots, where each robot in the swarm is capable of adjusting its time threshold parameters following the rules described by Liu et al. 2007. The swarm adapts the ratio of foragers to resters (division of labor) in order to maximize the net swarm energy for a given food density. A probabilistic finite state machine (PFSM) and a number of difference equations are developed to describe collective foraging at a macroscopic level. To model adaptation we introduce the new concepts of the sub-PFSM and private/public time thresholds. The model has been validated extensively with simulation trials, and results show that the model achieves very good accuracy in predicting the group performance of the swarm. Finally, a real-coded genetic algorithm is used to explore the parameter spaces and optimize the parameters of the adaptation algorithm. Although this paper presents a macroscopic probabilistic model for adaptive foraging, we argue that the approach could be applied to any adaptive swarm system in which the heterogeneity of the system is coupled with its time parameters.


Liu, W., & Winfield, A. F. (2010). Modeling and optimization of adaptive foraging in swarm robotic systems. International Journal of Robotics Research, 29(14), 1743-1760.

Journal Article Type Article
Online Publication Date Jul 23, 2010
Publication Date Dec 1, 2010
Deposit Date Sep 27, 2012
Publicly Available Date Feb 11, 2016
Journal International Journal of Robotics Research
Print ISSN 0278-3649
Electronic ISSN 1741-3176
Publisher SAGE Publications
Peer Reviewed Not Peer Reviewed
Volume 29
Issue 14
Pages 1743-1760
Keywords modelling, optimisation, adaptive foraging, swarm robotic systems
Public URL
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