caa a22 4500 605469695 CHVBK 20210128100323.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00500-014-1444-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1444-0 Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization [Elektronische Daten] [Hao Liu, Gang Xu, Guiyan Ding, Dawei Li] Bare-bones particle swarm optimization (BPSO) is attractive since it is parameter free and easy to implement. However, it suffers from premature convergence because of quickly losing diversity, and the dimensionality of the solved problems has great impact on the solution accuracy. To overcome these drawbacks, this paper proposes an opposition-based learning (OBL) modified strategy. First, to decrease the complexity of algorithm, OBL is not used for population initialization. Second, OBL is employed on the personal best positions (i.e., Pbest) to reconstruct Pbest, which is helpful to enhance convergence speed. Finally, we choose the global worst particle (Gworst) from Pbest, which simulates the human behavior and is called rebel learning item, and is integrated into the evolution equation of BPSO to help jump out local optima by changing the flying direction. The proposed modified BPSO is called BPSO-OBL, it has been evaluated on a set of well-known nonlinear benchmark functions in different dimensional search space, and compared with several variants of BPSO, PSOs and other evolutionary algorithms. Experimental results and statistic analysis confirm promising performance of BPSO-OBL on solution accuracy and convergence speed in solving majority nonlinear functions. Springer-Verlag Berlin Heidelberg, 2014 Particle swarm optimization (PSO) nationallicence Bare-bones PSO (BPSO) nationallicence Opposition-based learning nationallicence Evolutionary algorithm nationallicence Liu Hao School of Science, University of Science and Technology Liaoning, 114051, Anshan, China aut Xu Gang Department of Mathematics, Nan Chang University, 330031, Nanchang, China aut Ding Guiyan School of Science, University of Science and Technology Liaoning, 114051, Anshan, China aut Li Dawei School of Science, University of Science and Technology Liaoning, 114051, Anshan, China aut Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2813-2836 1432-7643 19:10<2813 2015 19 500 https://doi.org/10.1007/s00500-014-1444-0 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/s00500-014-1444-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Hao School of Science, University of Science and Technology Liaoning, 114051, Anshan, China aut NATIONALLICENCE 700 1- Xu Gang Department of Mathematics, Nan Chang University, 330031, Nanchang, China aut NATIONALLICENCE 700 1- Ding Guiyan School of Science, University of Science and Technology Liaoning, 114051, Anshan, China aut NATIONALLICENCE 700 1- Li Dawei School of Science, University of Science and Technology Liaoning, 114051, Anshan, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2813-2836 1432-7643 19:10<2813 2015 19 500