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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

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  • 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.

  • Keywords

Space Transformation Search (STS), evolutionary algorithm, Particle Swarm Optimization (PSO), optimization.

  • AMS Subject Headings

35R35, 49J40, 60G40

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-9-371, author = {S. Yu, Z. Wu, H. Wang, Z. Chen and H. Zhong}, 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 AU - S. Yu, Z. Wu, H. Wang, Z. Chen & H. Zhong 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 and H. Zhong. (2012). 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:
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