Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74389
Title: An improved brain storm optimization with learning strategy
Authors: Wang, H 
Liu, J
Yi, W
Niu, B
Baek, J
Keywords: Benchmark functions
Brain storm optimization
Improved BSO
Learning strategy
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10385, p. 511-518 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Brain Storm Optimization (BSO) algorithm is a brand-new and promising swarm intelligence algorithm by mimicking human being’s behavior of brainstorming. This paper presents an improved BSO, i.e., BSO with learning strategy (BSOLS). It utilizes a novel learning strategy whereby the first half individuals with better fitness values maintain their superiority by keeping away from the worst ones while other individuals with worse fitness values improve their performances by learning from the excellent ones. The improved algorithm is tested on 10 classical benchmark functions. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO and standard particle swarm optimization algorithm.
Description: 8th International Conference on Swarm Intelligence, ICSI 2017, 27 July 2017 - 1 August 2017
URI: http://hdl.handle.net/10397/74389
ISBN: 9783319618234
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-61824-1_56
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