A Quick Algorithm to Determine 2-Optimality Consensus for Collectives

Dang, Dai Tho; Nguyen, Ngoc Thanh; Hwang, Dosam

doi:10.1109/access.2020.3043371

Cited by 5 publications

(1 citation statement)

References 59 publications

(83 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relation between elements may be a distance function or preference relation between elements. Many structures of elements have been investigated, such as n-tree [13], ordered partitions [20], disjunction and conjunction Structures [29], binary vectors [30], and ontology [31], [32] The optimization approach defines consensus choice functions, which are usually based on optimality rules. Optimality rules include the global optimality rule, Condorcet's optimality rule, and maximal similarity rules [18].…”

Section: Related Workmentioning

confidence: 99%

An effective method for determining consensus in large collectives

2022

Self Cite

View full text Add to dashboard Cite

Nowadays, using the consensus of collectives for solving problems plays an essential role in our lives. The rapid development of information technology has facilitated the collection of distributed knowledge from autonomous sources to find solutions to problems. Consequently, the size of collectives has increased rapidly. Determining consensus for a large collective is very time-consuming and expensive. Thus, this study proposes a vertical partition method (VPM) to find consensus in large collectives. In the VPM, the primary collective is first vertically partitioned into small parts. Then, a consensus-based algorithm is used to determine the consensus for each smaller part. Finally, the consensus of the collective is determined based on the consensuses of the smaller parts. The study demonstrates, both theoretically and experimentally, that the computational complexity of the VPM is lower than 57.1% that of the basic consensus method. This ratio reduces quickly if the number of smaller parts reduces.

show abstract