A teaching-learning-based optimization algorithm with producer-scrounger model for global optimization

Verfasser / Beitragende:
[Debao Chen, Feng Zou, Jiangtao Wang, Wujie Yuan]
Ort, Verlag, Jahr:
2015
Enthalten in:
Soft Computing, 19/3(2015-03-01), 745-762
Format:
Artikel (online)
ID: 605469520
LEADER caa a22 4500
001 605469520
003 CHVBK
005 20210128100322.0
007 cr unu---uuuuu
008 210128e20150301xx s 000 0 eng
024 7 0 |a 10.1007/s00500-014-1298-5  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00500-014-1298-5 
245 0 2 |a A teaching-learning-based optimization algorithm with producer-scrounger model for global optimization  |h [Elektronische Daten]  |c [Debao Chen, Feng Zou, Jiangtao Wang, Wujie Yuan] 
520 3 |a In order to decrease the computation cost and improve the global performance of the original teaching-learning-based optimization (TLBO) algorithm, the area-copying operator of the producer-scrounger (PS) model is introduced into TLBO for global optimization problems. In the proposed method, the swarm is divided into three parts: the producer, scroungers and remainders. The producer is the best individual selected from current population and it exploits the new solution with a random angle and a maximal radius. Some individuals, which are different from the producer, are randomly selected according to a predefined probability as scroungers. The scroungers update their position with an area-copying operator, which is used in the PS model. The remainders are updated by means of teaching and learning operators as they are used in the TLBO algorithm. In each iteration, the computation cost of the proposed algorithm is less than that of the original TLBO algorithm, because the individuals of the PS model are only evaluated once and the individuals of the TLBO algorithm are evaluated two times in each iteration. The proposed algorithm is tested on different kinds of benchmark problems, and the results indicate that the proposed algorithm has competitive performance to some other algorithms in terms of accuracy, convergence speed and success rate. 
540 |a Springer-Verlag Berlin Heidelberg, 2014 
690 7 |a Teaching-learning-based optimization (TLBO)  |2 nationallicence 
690 7 |a Particle swarm optimization (PSO)  |2 nationallicence 
690 7 |a Global optimization  |2 nationallicence 
690 7 |a Benchmark problems  |2 nationallicence 
690 7 |a Producer-scrounger model  |2 nationallicence 
700 1 |a Chen  |D Debao  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
700 1 |a Zou  |D Feng  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
700 1 |a Wang  |D Jiangtao  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
700 1 |a Yuan  |D Wujie  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
773 0 |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/3(2015-03-01), 745-762  |x 1432-7643  |q 19:3<745  |1 2015  |2 19  |o 500 
856 4 0 |u https://doi.org/10.1007/s00500-014-1298-5  |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/s00500-014-1298-5  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Chen  |D Debao  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Zou  |D Feng  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Wang  |D Jiangtao  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Yuan  |D Wujie  |u School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/3(2015-03-01), 745-762  |x 1432-7643  |q 19:3<745  |1 2015  |2 19  |o 500