Quantum-inspired Genetic Algorithm with Local Search and Its Applications in Reactive Power Optimization
For the local search capability of quantum-inspired genetic algorithm (QGA) is Limited, a quantum-inspired genetic algorithm with local search (LSQGA) is presented to solve the reactive power optimization problem. This technique introduces local search into QGA for searching global solution. In the process of searching global solution, if the searched best solution is not improved in certain successive iterations, local search is applied to explore the neighbor domain of the solution. LSQGA has better global and local search capabilities simultaneously. Experiments are carried out on complex functions and IEEE30-bus system, and show that LSQGA is competitive to several other optimization methods such as novel quantum genetic algorithm and evolution strategy, in terms of search capability, convergence speed and stability.
power systemreactive power optimizationquantum-inspired genetic algorithm (QGA)quantum-inspired genetic algorithm with local search(LSQGA)
国家科技期刊平台
NSTL
万方数据
维普
