An Improved Particle Swarm Optimization Algorithm
Year: 2013
Type: article
Abstract: This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.
Source: Applied Mechanics and Materials
Authors: Guan Yu Zhang, Xiao Ming Wang, Rui Guo, Guo Qiang Wang
Institutions: Jilin University, Taiyuan Heavy Industry (China)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3150172359