Paper Info
Title
The Enhanced Vector of Convergence for Particle Swarm Optimization based on constrict factor
Abstract
The Particle Swarm Optimizer is used very widely for unimodal and multi-modal optimization problems. Recently, most of variant PSOs are combing several evolutionary strategies in order to achieve a better performance on Benchmark functions, and even for shifted, rotated, or composite functions. In this paper, a new method known as Enhanced Vector of Convergence is proposed and combined with constrict factor to improve the convergence performance of Particle Swarm Optimizer. In experimental study, other 5 variant Particle Swarm Optimizers are compared, and acceptance rate, t-Test are used for further evaluation. The results indicate that the Enhance Vector of Convergence can significantly improve the accurate level of Particle Swarm Optimizer.
Year
DOI
Venue
2014
10.1109/CEC.2014.6900392
IEEE Congress on Evolutionary Computation
Keywords
DocType
Citations 
unimodal optimization problem,acceptance rate,evolutionary computation,composite function,benchmark functions,enhanced vector-of-convergence,evolutionary strategies,pso,particle swarm optimisation,convergence performance improvement,rotated function,multimodal optimization problem,constrict factor,convergence,t-test,shifted function,particle swarm optimization,vectors,optimization,topology,acceleration,benchmark testing,t test
Conference
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Zhang Wei139253.03
Yanan Gao200.34
Chengxing Zhang300.34