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International Conference on Machine Learning, Control, and Robotics (MLCR)

Multi-population Quantum Genetic Algorithm Based on Optimal Computational Distribution

Xiangming Yang, Guanjie Jiang, Xiaoxiao Liu

Published Oct 1, 2022• 0 citations
Publisher

Abstract

Ordinary quantum genetic algorithm is easy to fall into local optimum in the process of complex function optimization, especially in the case of random sampling in which it is difficult to find a solution close to the true value. Therefore, a multi-group quantum genetic algorithm combining the idea of order optimization is proposed, and the number of simulations is reasonably allocated through the optimal calculation allocation technology, which further improves the convergence speed and reliability of the algorithm. Experiments show that a solution close to the true value by this proposed algorithm can be found efficiently.

DOI
10.1109/mlcr57210.2022.00024

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An improved quantum genetic algorithm(IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e. local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.
In this paper, a novel quantum genetic algorithm is proposed. This algorithm compares the probability expectation of the quantum chromosome with the best binary solution to determine rotation angle of rotation gate. Different individual in population evolve with different rate to complete local search and global search simultaneously. Hε gate is used to prevent the algorithm from premature convergence. After analyzing the algorithm and its global convergence, applying this approach to the optimization of function extremum, and comparing with the simple genetic algorithm and the quantum genetic algorithm, the simulation result illustrates that the algorithm has the characteristic of quick convergence speed and high solution precision.