Open AccessA Golden Section-based Double Population Genetic Algorithm Applied to Reactive Power Optimization
Author(s) -
Zhongyong Wang,
Yan Xu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/645/1/012074
Subject(s) - genetic algorithm , meta optimization , mathematical optimization , premature convergence , population , computer science , convergence (economics) , power (physics) , population based incremental learning , algorithm , optimization problem , exploit , global optimization , optimization algorithm , mathematics , computer security , physics , demography , quantum mechanics , sociology , economics , economic growth
In this paper, a more effective method-the improved double population genetic algorithm is applied to reactive power optimization. The proposed algorithm is aimed at optimization strategy which makes the population exploit in new space successively. Premature convergence and weak local optimization are two key problems existing in the conventional genetic algorithm. Based on the golden section, the double population genetic algorithm (DPGA) is optimized and applied to the reactive power optimization in power system. Numerical simulation results demonstrate the validity of the proposed design algorithm.