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A Two-stage State Transition Algorithm for Constrained Engineering Optimization Problems

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  • Control Theory and Applications
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Abstract

In this study, a state transition algorithm (STA) is investigated into constrained engineering design optimization problems. After an analysis of the advantages and disadvantages of two well-known constraint-handling techniques, penalty function method and feasibility preference method, a two-stage strategy is incorporated into STA, in which, the feasibility preference method is adopted in the early stage of an iteration process whilst it is changed to the penalty function method in the later stage. Then, the proposed STA is used to solve three benchmark problems in engineering design and an optimization problem in power-dispatching control system for the electrochemical process of zinc. The experimental results have shown that the optimal solutions obtained by the proposed method are all superior to those by typical approaches in the literature in terms of both convergency and precision.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, P. R. China

    Jie Han, Chunhua Yang, **aojun Zhou & Weihua Gui

Authors
  1. Jie Han
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  2. Chunhua Yang
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  4. Weihua Gui
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Corresponding author

Correspondence to **aojun Zhou.

Additional information

Recommended by Associate Editor Ho Jae Lee under the direction of Editor Yoshito Ohta. This work was supported by the National Natural Science Foundation (NNSF) of China (61503416, 61533021), the 111 Project (B17048), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 61621062) the Innovation-Driven Plan in Central South University, and the Independent Exploration Innovation Program for Postgraduate Students of Central South University(Grant No. 2017zzts136).

Jie Han received her Bachelor’s degree in Automation in 2014 from Central South University, Changsha, China and she is currently a Ph.D. student at Central South University, Changsha, China. Her main interests include optimization theory and algorithms, state transition algorithm, optimization and control of complex industrial process.

Chunhua Yang received her M.Eng. in Automatic Control Engineering and her Ph.D. in Control Science and Engineering from Central South University, China, in 1988 and 2002, respectively, and was with the Electrical Engineering Department, Katholieke Universiteit Leuven, Belgium from 1999 to 2001. She is currently a full professor in the School of Information Science & Engineering, Central South University. Her research interests include modeling and optimal control of complex industrial process, intelligent control system, and fault-tolerant computing of real-time systems.

**aojun Zhou received his Bachelor’s degree in Automation in 2009 from Central South University, Changsha, China and received the PhD degree in Applied Mathematics in 2014 from Federation University Australia. He is currently an Associate Professor at Central South University, Changsha, China. His main interests include modeling, optimization and control of complex industrial process, optimization theory and algorithms, state transition algorithm, duality theory and their applications.

Weihua Gui received the degree of the B.Eng.(Automatic Control Engineering) and the M.Eng. (Control Science and Engineering) from Central South University, Changsha, China, in 1976 and 1981, respectively. From 1986 to 1988 he was a visiting scholar at Universitat-GHDuisburg, Germany. He is a member of the Chinese Academy of Engineering and has been a full professor in the School of Information Science & Engineering, Central South University, Changsha, China, since 1991. His main research interests are in modeling and optimal control of complex industrial process, distributed robust control, and fault diagnoses.

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Han, J., Yang, C., Zhou, X. et al. A Two-stage State Transition Algorithm for Constrained Engineering Optimization Problems. Int. J. Control Autom. Syst. 16, 522–534 (2018). https://doi.org/10.1007/s12555-016-0338-6

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  • DOI: https://doi.org/10.1007/s12555-016-0338-6

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