Advanced discussion mechanism-based brain storm optimization algorithm

Verfasser / Beitragende:
[Yuting Yang, Yuhui Shi, Shunren Xia]
Ort, Verlag, Jahr:
2015
Enthalten in:
Soft Computing, 19/10(2015-10-01), 2997-3007
Format:
Artikel (online)
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024 7 0 |a 10.1007/s00500-014-1463-x  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00500-014-1463-x 
245 0 0 |a Advanced discussion mechanism-based brain storm optimization algorithm  |h [Elektronische Daten]  |c [Yuting Yang, Yuhui Shi, Shunren Xia] 
520 3 |a Evolutionary computation-based algorithms are successfully developed to handle challenges in optimization problems by applying the analogy to biological systems. We aim at designing advanced optimization algorithms, with inspiration from human's creative problem-solving strategies. In this paper, we proposed an advanced discussion mechanism-based brain storm optimization (ADMBSO) algorithm, pushing forward our study in the incorporation of inter- and intra-cluster discussions into the brain storm optimization algorithm (BSO) to control global and local searching ability, respectively. In the advanced discussion mechanism, elaborately designed inter- and intra-cluster discussions were alternatively performed throughout the optimization process, with the ratio controlled by a linearly adjusted probability. We further introduced a differential step strategy into the workflow, making ADMBSO a more efficient and more adaptive algorithm. Empirical studies on different function optimization problems illustrated the effectiveness and efficiency of the ADMBSO algorithm. Comparisons among the ADMBSO, BSO algorithm, closed-loop brain storm optimization algorithm, particle swarm optimization algorithm, and differential evolution algorithm, have also been provided in detail. As one of the first algorithms inspired by human behavior, ADMBSO demonstrates its great potential in dealing with complex optimization problems. 
540 |a Springer-Verlag Berlin Heidelberg, 2014 
690 7 |a Global optimization  |2 nationallicence 
690 7 |a Evolutionary computation  |2 nationallicence 
690 7 |a Brain storm optimization  |2 nationallicence 
690 7 |a Discussion mechanism  |2 nationallicence 
690 7 |a Differential step  |2 nationallicence 
700 1 |a Yang  |D Yuting  |u Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, 310027, Hangzhou, China  |4 aut 
700 1 |a Shi  |D Yuhui  |u Xi'an Jiaotong-Liverpool University, 215123, Suzhou, China  |4 aut 
700 1 |a Xia  |D Shunren  |u Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, 310027, Hangzhou, China  |4 aut 
773 0 |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/10(2015-10-01), 2997-3007  |x 1432-7643  |q 19:10<2997  |1 2015  |2 19  |o 500 
856 4 0 |u https://doi.org/10.1007/s00500-014-1463-x  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s00500-014-1463-x  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Yang  |D Yuting  |u Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, 310027, Hangzhou, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Shi  |D Yuhui  |u Xi'an Jiaotong-Liverpool University, 215123, Suzhou, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Xia  |D Shunren  |u Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, 310027, Hangzhou, China  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/10(2015-10-01), 2997-3007  |x 1432-7643  |q 19:10<2997  |1 2015  |2 19  |o 500