DocumentCode
2732380
Title
Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization
Author
Chen, Guimin ; Huang, Xinbo ; Jia, Jianyuan ; Min, Zhengfeng
Author_Institution
Sch. of Electronical & Mech. Eng., Xidian Univ., Xi´´an
Volume
1
fYear
0
fDate
0-0 0
Firstpage
3672
Lastpage
3675
Abstract
Inertia weight is one of the most important parameters of particle swarm optimization (PSO) algorithm. Based on the basic idea of decreasing inertia weight (DIW), two strategies of natural exponential functions were proposed. Four different benchmark functions were used to evaluate the effects of these strategies on the PSO performance. The results of the experiments show that these two new strategies converge faster than linear one during the early stage of the search process. For most continuous optimization problems, these two strategies perform better than the linear one
Keywords
particle swarm optimisation; continuous optimization problem; decreasing inertia weight; natural exponential function; natural exponential inertia weight; particle swarm optimization; search process; Acceleration; Birds; Collaboration; Equations; Fuzzy sets; Fuzzy systems; Mechanical engineering; Particle swarm optimization; Random number generation; Inertia Weight; Particle Swarm Optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location
Dalian
Print_ISBN
1-4244-0332-4
Type
conf
DOI
10.1109/WCICA.2006.1713055
Filename
1713055
Link To Document