.png){kind=icon}
返回
Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.simpat.2011.01.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Agent-based distributed distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution. (C) 2011 Elsevier BM. All rights reserved.
Keyword:
Agent
Distributed simulation
Time synchronization mechanism
Dynamic load balancing
Agent scheduling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.6
论文数:
2.6K
被引数:
4.8K
机构
引用论文
.png){kind=icon}
Agent-based intelligent system development for decision support in chemical process industry基于Agent的化工过程工业决策支持智能系统开发
PREAI
分享收藏
Agent framework services to reduce agent communication overhead in large-scale agent-based simulations代理框架服务，以减少大规模基于代理的仿真中的代理通信开销
PREAI
分享收藏
查看更多内容