An Improved PBIL Algorithm for Optimal Coalition Structure Generation of Smart Grids

被引：0

作者：

Lee, Sean Hsin-Shyuan ^{[1
]}

Deng, Jeremiah D. ^{[1
]}

Purvis, Martin K. ^{[1
]}

Purvis, Maryam ^{[1
]}

Peng, Lizhi ^{[2
]}

机构：

[1] Univ Otago, Dept Informat Sci, Dunedin, New Zealand

[2] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan, Peoples R China

来源：

TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING: PAKDD 2018 WORKSHOPS | 2018年 / 11154卷

关键词：

Coalition structure generation; Smart grids; Optimization; Dynamic programming; Population-based incremental learning;

D O I：

10.1007/978-3-030-04503-6_33

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Coalition structure generation in multi-agent systems has long been a challenging problem because of its NP-hardness in computational complexity. In this paper, we propose a stochastic optimization approach that employs a modified population based incremental learning algorithm and a customized genotype encoding scheme to find the optimal coalition structure for smart grids with renewable energy sources. Empirical results show that the proposed approach gives competitive performance compared with existing solutions such as genetic algorithm and dynamic programming.

引用

页码：345 / 356

页数：12

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