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《Proceedings of the CSEE》 2008-02
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An Improved Genetic Algorithm in Multi-objective Optimization and Its Application

ZHAO Liang, JU Gang, Lü Jian-hong (School of Energy and Environment, Southeast University, Nanjing 210096, Jiangsu Province, China)  
To tackle the multi-objective and multi- restriction optimization problems, both non-dominated ranking method and elitist preserve strategy were integrated into the conventional niche approaches for the improvement of individual adaptive function based wash out rule. As a result, an advanced Pareto multi-objective optimization with genetic algorithm was developed based on a novel vector module adaptive function. With the minimization of two duality functions, the proposed method is shown to be able to avoid the local convergence and achieve optimal solutions with more uniform distribution. Furthermore, this algorithm was applied to the PID controller parameters tuning in the boil superheated steam temperature cascade control system. The simulation results show that the improved multi-objective genetic algorithm (GA) optimization can obtain different optimal controller parameters with respect to the corresponding performance targets. And the proposed method is demonstrated to be competent even in the presence of plant dynamics deviation.
【Fund】: 国家863高技术基金项目(2006AA05A107)。~~
【CateGory Index】: TP18
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