NDLI: Improved Mind Evolutionary Algorithm Design Using Group Migration

Content Provider	IEEE Xplore Digital Library
Author	Fang Wang Keming Xie Jianxia Liu
Copyright Year	2009
Abstract	Mind evolutionary algorithm (MEA) is a kind of evolutionary algorithm, simulating the human’s mind development. It has two operators, similartaxis and dissimilation. Swarm intelligence attempts to design algorithms or distributed problem-solving devices without concentrated control and global model. This paper is intrigued by the advantages of swarm intelligence, presents the mechanisms of the improved MEA and designs refreshing rule of information density and the group migration strategy according to share rule of social information, which delicately balance between good solution exploitation and new solution exploration, fastens convergence velocity and obtains global optimum. The performance on four different test functions is qualitatively analyzed. Experimental results show that the improved MEA with group migration based on swarm intelligence can rapidly converge at high quality solution with high stability and precision.
Starting Page	292
Ending Page	296
File Size	3687519
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.395
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computational modeling Mind Evolutionary Algorithm (MEA) Evolutionary computation Educational institutions Birds Telecommunication computing function optimization Particle swarm optimization Design engineering group migration Marine animals swarm intelligence Testing
Content Type	Text
Resource Type	Article

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