Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

Zhiming Lv; Linqing Wang; Zhongyang Han; Jun Zhao; Wei Wang

doi:10.1109/JAS.2019.1911450

Volume 6 Issue 3

May 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(3): 838-849

Zhiming Lv, Linqing Wang, Zhongyang Han, Jun Zhao and Wei Wang, "Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 838-849, May 2019. doi: 10.1109/JAS.2019.1911450

Citation:

Zhiming Lv, Linqing Wang, Zhongyang Han, Jun Zhao and Wei Wang, "Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 838-849, May 2019. doi: 10.1109/JAS.2019.1911450

Citation:

Zhiming Lv, Linqing Wang, Zhongyang Han, Jun Zhao and Wei Wang, "Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 838-849, May 2019. doi: 10.1109/JAS.2019.1911450

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Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

doi: 10.1109/JAS.2019.1911450

Funds: This work was supported by the National Natural Sciences Foundation of China (61603069, 61533005, 61522304, U1560102) and the National Key Research and Development Program of China (2017YFA0700300)

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Author Bio:
Zhiming Lv graduated from Shenyang University of Chemical Technology, China, in 2006. He received the M.S. degree from Shenyang University of Chemical Technology in 2010. He is currently working toward Ph.D. degree at the School of Control Science and Engineering, Dalian University of Technology, Dalian, China. His research interests include industrial process modeling, optimization scheduling, and intelligent optimization algorithm

Linqing Wang graduated from Northeastern University (NEU), China, in 2007. She received the M.S. degree and Ph.D. degree from NEU in 2010 and 2014, respectively. She is currently a Senior Lecturer at the School of Control Sciences and Engineering, Dalian University of Technology. Her research interests include the prediction algorithm, industrial optimization, and intelligent algorithm

Zhongyang Han graduated from City Institute, Dalian University of Technology, China, in 2010. He received the Ph.D. degree from Dalian University of Technology, China, in 2016. He is currently engaging in the postdoctoral research with the School of Control Science and Engineering, Dalian University of Technology, Dalian, China. His research interests include computer integrated manufacturing systems, artificial intelligence, data mining, and machine learning

Jun Zhao (M’10) graduated from Dalian Jiaotong University, China, in 2003. He received the Ph.D. degree in engineering from Dalian University of Technology, in 2008. He is currently a Professor with the School of Control Science and Engineering, Dalian University of Technology. His current research interests include industrial production scheduling, computer integrated manufacturing, intelligent optimization, and machine learning

Wei Wang (SM’13) received the B.S., M.S., and Ph.D. degrees from Northeastern University, Shenyang, China, in 1982, 1986, and 1988, respectively, all in industrial automation. He was a postdoctoral fellow with the Division of Engineering Cybernetics, Norwegian Science and Technology University, Trondheim, Norway, from 1990 to 1992, a Professor and Vice Director of the National Engineering Research Center of Metallurgical Automation of China, China, from 1995 to 1999, and a Research Fellow with the Department of Engineering Science, University of Oxford, Oxford, UK, from 1998 to 1999. He is currently a Professor with the School of Control Sciences and Engineering, Dalian University of Technology, China. His current research interests include adaptive controls, computer integrated manufacturing, and computer controls of industrial processes
Corresponding author: All the authors are with the Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China (e-mail: lv_zhiming@126.com; wanglinqing@dlut.edu.cn; hzydut@mail.dlut.edu.cn; zhaoj@dlut.edu.cn; wangwei@dlut.edu.cn)
Received Date: 2018-08-24
Revised Date: 2018-12-12
Accepted Date: 2019-01-07

Available Online: 2019-04-24

Abstract

Abstract

For multi-objective optimization problems, particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space (the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, $ \varepsilon $-Pareto active learning ($ \varepsilon $-PAL) method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter $ \varepsilon $. Therefore, a greedy search method is presented to determine the value of $ \varepsilon $ where the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines (MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization (MOPSO) algorithms.
- Multiobjective optimization,
- Pareto active learning,
- particle swarm optimization (PSO),
- surrogate

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Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

doi: 10.1109/JAS.2019.1911450

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