Abstract：To deal with the problems of easily falling into local optimum and low accuracy in the quantum evolutionary algorithm, a real-coded quantum evolutionary algorithm based on cloud model (CRCQEA) was proposed by using the characteristics of cloud model randomness and stable disposition. The algorithm used a single-dimensional variation of cloud for rapid global search, and used a multi-dimensional cloud evolution for enhancing local search ability to explore the global optimal solution. Dynamic adjustment of search range and resetting of the chromosomes, on the basis of the evolutionary process of algorithm, can speed up the convergence and prevent falling into local optimum. The simulation results show that the algorithm improves search accuracy and efficiency, and the algorithm is well suitable for the complex function optimization.
2013, Vol. 33 
