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IEEE International Conference on Electronics and Communication Engineering (ICECE)

Progress, Application and Prospect of Quantum Genetic Algorithm

Liu Shuguang, Lin Ba

Published Dec 1, 2018• 2 citations
Publisher

Abstract

The quantum genetic algorithm (QGA) is derived from the integration of quantum computation and genetic algorithm. It is characterized with advantages like strong global optimization ability, fast convergence speed and small population size. In this paper, the principle, method and basic flow of quantum genetic algorithm (QGA) are introduced, and the research progress of QGA in recent years is reviewed, including the coding extension of theoretical basis, the innovation of operators, the rotation angle of quantum gates, the optimization of complex high-dimensional functions, and hybrid algorithms, and then he application status of QGA is also discussed. Finally, the future development direction of QGA is put forward.

DOI
10.1109/icecome.2018.8644838

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Aiming at the problems that the Quantum Genetic Algorithm(QGA) easily falls into local extremum,an improved QGA algorithm of quantum revolving gate is proposed.In order to achieve the goal of optimization solving,quantum bit probability amplitude is applied to the chromosome encoding,and quantum rotation gate is used to chromosome updating.The theoretical derivation proves that the proposed algorithm converges with probability 1,and the algorithm converges strongly to 1?e.Comparing with double QGA,simulation results show that the algorithm increases complexity,the average time extension,but convergence times increase,convergence times of the function 1 increases from 3 to 7,and convergence times of the function 2 increases from 8 to 9.
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Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.