.png){kind=icon}
Return
A local cost simulation-based algorithm to solve distributed constraint optimization problems
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.1296.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As an important incomplete algorithm for solving Distributed Constraint Optimization Problems (DCOPs), local search algorithms exhibit the advantages of flexibility, high efficiency and high fault tolerance. However, the significant historical values of agents that affect the local cost and global cost are never taken into in existing incomplete algorithms. In this article, a novel Local Cost Simulation-based Algorithm named LCS is presented to exploit the potential of historical values of agents to further enhance the exploration ability of the local search algorithm. In LCS, the Exponential Weighted Moving Average (EWMA) is introduced to simulate the local cost to generate the selection probability of each value. Moreover, populations are constructed for each agent to increase the times of being selected inferior solutions by population optimization and information exchange between populations. We theoretically analyze the feasibility of EWMA and the availability of solution quality improvement. In addition, based on our extensive empirical evaluations, we experimentally demonstrate that LCS outperforms state-of-the-art DCOP incomplete algorithms.
Keywords:
DCOPs
LCS
EWMA
Inferior solutions
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
3.4K
Citations:
6.9K
Organization
Cited Papers
Age, Experience and the Development of Adaptive Variation in Anti‐predator Responses in Three‐spined Sticklebacks (Gasterosteus aculeatus).png)
Ethology
EthologyIF0
PREAI
ShareSaveShareSaveShareSave
View more