arrow
Volume 9, Issue 2
A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model

S. Yu, Z. Wu, H. Wang, Z. Chen & H. Zhong

Int. J. Numer. Anal. Mod., 9 (2012), pp. 371-377.

Published online: 2012-09

Export citation
  • Abstract

Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO often easily falls into local optima because the particles would quickly get closer to the best particle. Under these circumstances, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by combining space transformation search (STS) with a new modified velocity model. Experimental studies on 8 benchmark functions demonstrate that the HPSO holds good performance in solving both unimodal and multimodal functions optimization problems.

  • AMS Subject Headings

35R35, 49J40, 60G40

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{IJNAM-9-371, author = {}, title = {A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2012}, volume = {9}, number = {2}, pages = {371--377}, abstract = {

Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO often easily falls into local optima because the particles would quickly get closer to the best particle. Under these circumstances, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by combining space transformation search (STS) with a new modified velocity model. Experimental studies on 8 benchmark functions demonstrate that the HPSO holds good performance in solving both unimodal and multimodal functions optimization problems.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/634.html} }
TY - JOUR T1 - A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model JO - International Journal of Numerical Analysis and Modeling VL - 2 SP - 371 EP - 377 PY - 2012 DA - 2012/09 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/634.html KW - Space Transformation Search (STS), evolutionary algorithm, Particle Swarm Optimization (PSO), optimization. AB -

Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO often easily falls into local optima because the particles would quickly get closer to the best particle. Under these circumstances, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by combining space transformation search (STS) with a new modified velocity model. Experimental studies on 8 benchmark functions demonstrate that the HPSO holds good performance in solving both unimodal and multimodal functions optimization problems.

S. Yu, Z. Wu, H. Wang, Z. Chen & H. Zhong. (1970). A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model. International Journal of Numerical Analysis and Modeling. 9 (2). 371-377. doi:
Copy to clipboard
The citation has been copied to your clipboard