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Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes

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Abstract

In order to maximize the amount of the final product while reducing the amount of the by-product in batch process, an improved multi-objective particle swarm optimization based on Pareto-optimal solutions is proposed in this paper. A novel diversity preservation strategy that combines the information of distance and angle into similarity judgment is employed to select global best and thus the convergence and diversity of the Pareto front is guaranteed. As a result, enough Pareto solutions are distributed evenly in the Pareto front. To test the effectiveness of the proposed algorithm, some benchmark functions are used and a comparison with its conventional counterparts is made. Furthermore, the algorithm is applied to two classical batch processes. The results show that the quality at the end of each batch can approximate the desire value sufficiently and the input trajectory converges, thus verify the efficiency and practicability of the proposed algorithm.

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Acknowledgments

Supported by National Natural Science Foundation of China (61004019), Research Fund for the Doctoral Program of Higher Education of China (20093108120013), Shanghai Science Technology commission (08160512100 and 09JC1406300), Grant from Shanghai Municipal Education commission (09YZ08), Shanghai University, “11th Five-Year Plan” 211 Construction Project and Innovation Project for Postgraduate Student Granted by Shanghai University (SHUCX102221).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Li Jia & Dashuai Cheng

  2. Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Min-Sen Chiu

Authors
  1. Li Jia
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  2. Dashuai Cheng
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  3. Min-Sen Chiu
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Correspondence to Li Jia.

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Jia, L., Cheng, D. & Chiu, MS. Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes. Neural Comput & Applic 21, 1107–1116 (2012). https://doi.org/10.1007/s00521-011-0659-6

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  • DOI: https://doi.org/10.1007/s00521-011-0659-6

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