caa a22 4500 605469520 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1298-5 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1298-5 A teaching-learning-based optimization algorithm with producer-scrounger model for global optimization [Elektronische Daten] [Debao Chen, Feng Zou, Jiangtao Wang, Wujie Yuan] 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. Springer-Verlag Berlin Heidelberg, 2014 Teaching-learning-based optimization (TLBO) nationallicence Particle swarm optimization (PSO) nationallicence Global optimization nationallicence Benchmark problems nationallicence Producer-scrounger model nationallicence Chen Debao School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut Zou Feng School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut Wang Jiangtao School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut Yuan Wujie School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 745-762 1432-7643 19:3<745 2015 19 500 https://doi.org/10.1007/s00500-014-1298-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1298-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Debao School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut NATIONALLICENCE 700 1- Zou Feng School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut NATIONALLICENCE 700 1- Wang Jiangtao School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut NATIONALLICENCE 700 1- Yuan Wujie School of Physics and Electronic Information, HuaiBei Normal University, 235000, Huaibei, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 745-762 1432-7643 19:3<745 2015 19 500