| Quantum genetic algorithm is a probabilistic searching algorithm which combines the concept and idea of quantum computing with the iterative evolution theory of genetic algorithm. This paper presents an improved quantum generic algorithm (IQGA), compared with the original quantum generic algorithm which is complex and occupies plenty of time in updating process. The improved algorithm uses single real number coding instead of the pair of real numbers in original algorithm for reducing the memory storage, and the updating process of chromosomes are simplified from the matrix multiplied by the vector to the step addition or subtraction in order to decrease the calculation. These improvements greatly increase its execution speed,and make the algorithm more concise and easy to implement.This paper designs a new method called test rotation gate method which based on the quantum uncertainty of quantum genetic algorithm fur solving the constraint optimization problems.The new method repeatedly measures the chromosomes in non-feasible region until the chromosomes satisfy the constraints that every chromosome is in feasible region, and execute the operation of test rotation gate. In addition,the new method improved the mutation section of the algorithm. The test results verify that the improved quantum genetic algorithm of the new method have excellent optimize performance in solving the constraint optimization problems.Finally, through study on the problem of load optimal dispatching in thermal power plant, this paper establishes its mathematic model and determines its constraint conditions. The optimization scheme and the details in solving the problem of load optimal dispatching in thermal power plant are completed, and the effeciency of the improved quantum algorithm in solving the problem of load optimal dispatching in thermal power plant is proved by the simulation results. |
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