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J. E. Baker, “Reducing bias and inefficiency in the selection algorithm,” Proceedings of the 2nd Annual Conference on Genetic Algorithms, Massachusetts Institute of Technology, Cambridge, 1985, pp. 14-21.

has been cited by the following article:

• TITLE: An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization

  AUTHORS: Ouyang Sen

  KEYWORDS: Genetic Algorithms, Reactive Power Optimization, Catastrophe, Power System

  JOURNAL NAME: Energy and Power Engineering, Vol.2 No.4, November 26, 2010

  ABSTRACT: This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.