《计算机应用研究》|Application Research of Computers

量子光学优化算法

Quantum-behaved optics inspired optimization

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作者 王金叶,马良,刘勇
机构 上海理工大学 管理学院,上海 200093
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文章编号 1001-3695(2018)03-0654-04
DOI 10.3969/j.issn.1001-3695.2018.03.003
摘要 通过分析光学优化算法的特性,将光学优化算法中每个光源点都用量子空间中的一个粒子来描述,利用群体智慧的聚集性,建立了光学优化算法的量子势能场模型,并根据势能场模型的群体自组织性和协同性等特点提出了量子光学优化算法。通过对多个经典测试函仿真分析,得出量子光学优化算法在量子力学收敛理论下比光学优化算法控制参数少,设置简单,优化性能更好,收敛速度更快,优化了算法的收敛精度和速度。
关键词 量子力学;光学优化算法;量子势能场;仿真分析;优化性能
基金项目 国家自然科学基金资助项目(71401106)
国家教育部人文社科规划基金项目(16YJA630037)
上海市高原学科建设项目
上海高校青年教师培养计划资助项目(ZZsl15018)
上海理工大学博士科研启动经费项目(1D-15-303-005)
本文URL http://www.arocmag.com/article/01-2018-03-003.html
英文标题 Quantum-behaved optics inspired optimization
作者英文名 Wang Jinye, Ma Liang, Liu Yong
机构英文名 BusinessSchool,UniversityofShanghaiforScience&Technology,Shanghai200093,China
英文摘要 By analyzing the character of optics optimization algorithm, this paper described every light point in the algorithm as a particle in the quantum space and found the quantum potential field model with the aggregation of swarm intelligence. Because of the points of self-organize and cooperation, this paper raised the quantum-behaved optics algorithm. Using several functions to analyze, the results show that the algorithm with the theory of quantum mechanics has fewer control parameters and simpler setup, and it has better performance and faster convergent speed than OIO algorithms.
英文关键词 quantum mechanics; optics inspired optimization(OIO); quantum potential energy field; simulation analysis; performance of optimization
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收稿日期 2016/12/3
修回日期 2017/1/17
页码 654-657
中图分类号 TP310.6
文献标志码 A