中文

Journal of Applied Sciences ›› 2010, Vol. 28 ›› Issue (1): 83-89.

• Control and System • Previous Articles     Next Articles

Improved Multi-objective Genetic Algorithm with Application to PID Optimization Design

LIU Nan-nan1, SHI Yu1, CHENG Wei-ping2, QIN Fu-gao3;4   

  1. 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
    Nanjing 210016, China
    2. Design Institute of China Aviation, China Helicopter Research and Development Institute,
    Jingdezhen 333001, Jiangxi Province, China
    3. School of Computer and Information Engineering, Changzhou Institute of Technology, Changzhou 213002,
    Jiangsu Province, China
    4. College of Computer and Information Engineering, Hohai University, Nanjing 210098, China
  • Received:2009-06-16 Revised:2009-10-10 Online:2010-01-20 Published:2010-01-20

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Abstract:

We propose a multi-objective optimization genetic algorithm, which uses a new method to calculate crowding distance and improves the comparative method of non-domination. Double elitism-mechanism is introduced to improve efficiency of evolution and solution quality, and more effectively increase diversity of the solution. The algorithm is applied to optimal design of PID. In this way, the system is capable of considering
requirements for quickness, reliability and robustness. A satisfactory solution is selected in Pareto optimum set according to the requirements of the present system. Simulation results indicate effectiveness of the proposed algorithm.

Key words: multi-objective optimization, genetic algorithms, Pareto optimal solution, PID control

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