Jeffery K. Cochran, Shwu-Min Horng, John W. Fowler. “A Multi-population Genetic Algorithm to Solve Multi-objective Scheduling Problems for Parallel Machines,” Computers & Operations Research, June 2003, 30(7): 1087–1102.

has been cited by the following article:

• TITLE: Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

  AUTHORS: Hua Xiao, Cheng Yang, Jie Wu, Xiaoqian Ma

  KEYWORDS: Gas turbine generation units; Load dispatch; MPGA; Optimization

  JOURNAL NAME: Engineering, Vol.5 No.1B, January 16, 2013

  ABSTRACT: In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.