Abstract
Multi-option collective decision making is an emergent topic of study within the field of swarm intelligence. Many strategies have been proposed to enable decentralized and localized decision-making behaviors in intelligent swarms. However, many proposed strategies have very different requirements on the communication bandwidth and paradigm, which make a clear and fair comparison difficult. In this paper, we seek to investigate the performances of several promising decision-making algorithms in a discrete collective estimation scenario when the communication bandwidth and paradigm are controlled. The considered algorithms’ performances are gauged via error, consensus time and failure rate. Among the considered algorithms, we have observed that distributed Bayesian belief sharing (DBBS) has superior performances in all three metrics, especially at higher communication bandwidths. On the other hand, ranked voting with Borda count (RV-BC) has comparable performances to the baseline algorithms at lower bandwidths, while slightly outperforms at higher bandwidths. We have concluded that the direct belief fusion mechanism that underpins DBBS is an efficient use of communication bandwidths in the experimental scenario investigated here. However, among the considered algorithms, its message size scales the quickest with the number of available options, which can potentially limit its viability.
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Shan, Q., Mostaghim, S. (2022). Benchmarking Performances of Collective Decision-Making Strategies with Respect to Communication Bandwidths in Discrete Collective Estimation. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_5
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