NDLI: A learning strategy for multi-robot based on probabilistic evolutionary algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jiancong Fan Yongquan Liang Jiuhong Ruan
Copyright Year	2010
Description	Author affiliation: Scientific Research Department, Shandong Jiaotong University, Jinan 250023, China (Jiuhong Ruan) \|\| College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China (Jiancong Fan; Yongquan Liang)
Abstract	Estimation of distribution algorithm (EDA) is a new evolutionary computation method based on probabilistic theory. EDA can select optimal individuals through estimating probability distribution function of a population. The capture problem among multi software robots can be solved by EDA. The capture problem involves that some pursuers pursue several evaders through part of trajectory. The trajectory was produced by the evaders during their two-dimensional random mobility. The pursuers estimate the evaders' mobility functions and adjust their pursuit models to capture the evaders as fast as possible. The probabilistic evolutionary courses of multi-robot experiencing some competitions are analyzed in performances. The analysis shows that capture problem of multi-robot solved by EDA is better than other methods in several aspects.
Starting Page	3275
Ending Page	3280
File Size	1157779
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5555034
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probabilistic logic Evolutionary computation Estimation Trajectory Information science Computational modeling Software capture course evolutionary computation estimation of distribution algorithm multi-robot competition
Content Type	Text
Resource Type	Article

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