Search

Genetic algorithm( GA) has the defect of prematurity during the optimization process which makes it easy to fall into the local minimum. To cope with this defect, the author proposes improved cloud adaptive genetic algorithm( ICAGA). Bying applying GA in the cloud model, the adaptive adjustment of the crossover and mutation probability can be done by X condition generator, which enjoys both traditional AGA trend and the fast optimization with randomness. The algorithm can aslo improve the capacity to avoid falling into local optimal. Then the idea of shrinking search in the Simulating Fisher fishing Optimization Algorithm( SFOA) is brouhgt in to obtain the global optimaization. Taking the minimum network loss as objective function, the simulation for the proposed ICAGA algorithm by standard IEEE 14- bus system and IEEE 30- bus system are performed. The simulation results show that the better optimal solution can be attained by the proposed ICAGA algorithm. [ABSTRACT FROM AUTHOR]