[1]曹茂俊,尤文菁,卢玉莹.基于自寻优和交叉寻优的量子优化算法[J].计算机技术与发展,2022,32(07):8-14.[doi:10. 3969 / j. issn. 1673-629X. 2022. 07. 002]
　CAO Mao-jun,YOU Wen-jing,LU Yu-ying.uantum Optimization Algorithm Based on Self-optimization and Cross-optimization[J].,2022,32(07):8-14.[doi:10. 3969 / j. issn. 1673-629X. 2022. 07. 002]
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基于自寻优和交叉寻优的量子优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年07期
页码:
8-14
栏目:
人工智能
出版日期:
2022-07-10

文章信息/Info

Title:
uantum Optimization Algorithm Based on Self-optimization and Cross-optimization
文章编号:
1673-629X(2022)07-0008-07
作者:
曹茂俊; 尤文菁; 卢玉莹
东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
CAO Mao-jun; YOU Wen-jing; LU Yu-ying
School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China
关键词:
优化算法; 自寻优; 交叉寻优; 量子衍生算法; Bloch 球面坐标
Keywords:
optimization algorithm; self-optimization; cross-optimization; quantum inspired algorithm; Bloch coordinates
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 07. 002
摘要:
为提高群智能优化算法的搜索能力,提出了一种量子优化算法。? 该算法基于量子位 Bloch 坐标,将种群分为最优个体和普通个体两部分并进行不同处理,对于最优个体,通过使所有量子比特在 Bloch 球面上绕着坐标轴多次旋转,生成多个新个体,并采用贪婪搜索策略选择最优个体;对于普通个体,将当前个体的量子比特向着随机交叉确定的目标位置旋转,生成新个体,并在当前个体和新个体之间通过贪婪选择以实现当前个体的交叉寻优。 函数极值优化的仿真结果表明,所提算法在优化能力上, 优于简单量子遗传算法、普通遗传算法和人工鱼群算法,从而验证了算法的有效性。 该算法在高维能很好地避免陷入局部最优值,具有快速收敛性和良好的全局搜索能力,实验结果揭示出采用量子计算设计优化算法进而提升搜索能力的研究思路是可行的。
Abstract:
To improve the search capability of the group intelligence optimization algorithm, a quantum optimization algorithm? ? ? ? ? ?is proposed. Based on Bloch coordinates, the algorithm divides the population into optimal individuals and ordinary individuals and performs different processing. For the optimal individual,by rotating all qubits around the coordinate axis,multiple new individuals are generated and using the greedy search strategy,the optimal individual is selected. For current individuals in the population,the cross-optimization of the current individual by determining random crossings? ? with other individuals and rotating the qubits towards the target position generates a new individual with greedy selection between the current individual and the new individual. Simulation results of functional extremal optimization show that the proposed algorithm outperforms the simple quantum genetic algorithm,the common genetic algorithm and the artificial fish swarms algorithm in terms of optimization ability,thus verifying the effectiveness of such algorithm. The algorithm avoids falling into local optimum in high dimensions, with fast convergence and strong global search capability. The experimental results reveal that the optimization algorithm is feasible.

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更新日期/Last Update: 2022-07-10