Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74173
Title: Distributed learning particle swarm optimizer for global optimization of multimodal problems
Authors: Zhang, G
Li, Y 
Shi, Y
Keywords: Orthogonal experimental design (OED)
Particle swarm optimizer (PSO)
Swarm intelligence
Issue Date: 2018
Publisher: Higher Education Press
Source: Frontiers of computer science, 2018, v. 12, no. 1, p. 122-134 How to cite?
Journal: Frontiers of computer science 
Abstract: Particle swarm optimizer (PSO) is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimumwhen solving complex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer (DLPSO) to solve multimodal nonseparable problems. The strategy for DLPSO is to extract good vector information from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimental studies on a set of test functions show that DLPSO exhibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.
URI: http://hdl.handle.net/10397/74173
ISSN: 2095-2228
EISSN: 2095-2236
DOI: 10.1007/s11704-016-5373-1
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