International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 98 - 129

Enhanced Particle Swarm Optimization Based on Reference Direction and Inverse Model for Optimization Problems

Authors
Wei Li1, 2, *, Yaochi Fan1, Qingzheng Xu3
1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China
2Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an 710048, China
3College of Information and Communication, National University of Defense Technology, Xi'an 710106, China
*Corresponding author. Email: liwei@xaut.edu.cn
Corresponding Author
Wei Li
Received 5 October 2019, Accepted 22 January 2020, Available Online 3 February 2020.
DOI
10.2991/ijcis.d.200127.001How to use a DOI?
Keywords
Particle swarm optimization; Reference direction; Non-dominated sorting; Inverse model; Neural networks optimization
Abstract

While particle swarm optimization (PSO) shows good performance for many optimization problems, the weakness in premature convergence and easy trapping into local optimum, due to the ignorance of the diversity information, has been gradually recognized. To improve the optimization performance of PSO, an enhanced PSO based on reference direction and inverse model is proposed, RDIM-PSO for short reference. In RDIM-PSO, the reference particles which are used as reference directions are selected by non-dominated sorting method according to the fitness and diversity contribution of the population. Dynamic neighborhood strategy is introduced to divide the population into several sub-swarms based on the reference directions. For each sub-swarm, the particles focus on exploitation under the guidance of local best particle with a good guarantee of population diversity. Moreover, Gaussian process-based inverse model is introduced to generate equilibrium particles by sampling the objective space to further achieve a good balance between exploration and exploitation. Experimental results on CEC2014 test problems show that RDIM-PSO has overall better performance compared with other well-known optimization algorithms. Finally, the proposed RDIM-PSO is also applied to artificial neural networks and the promising results on the chaotic time series prediction show the effectiveness of RDIM-PSO.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
98 - 129
Publication Date
2020/02/03
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200127.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Wei Li
AU  - Yaochi Fan
AU  - Qingzheng Xu
PY  - 2020
DA  - 2020/02/03
TI  - Enhanced Particle Swarm Optimization Based on Reference Direction and Inverse Model for Optimization Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 98
EP  - 129
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200127.001
DO  - 10.2991/ijcis.d.200127.001
ID  - Li2020
ER  -