| 一种基于浓度调节的改进型量子遗传算法 |
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| 引用本文： | 胡小祥,刘漫丹. 一种基于浓度调节的改进型量子遗传算法[J]. 医学教育探索, 2016, 0(5): 690-695 |
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| 作者姓名： | 胡小祥 刘漫丹 |
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| 作者单位： | 华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237,华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237 |
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| 基金项目： | 中央高校基本科研业务费专项资金（WH1213010） |
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| 摘 要： | 针对量子遗传算法（QGA）优化多峰函数时存在收敛速度慢、容易陷入局部最优的缺陷，提出了改进型量子遗传算法（IQGA）。引入个体浓度的概念，在量子门更新之前对种群进行筛选并剔除高浓度个体和劣个体，并用新的个体代替它们，增强了量子遗传算法全局搜索能力。通过典型复杂连续函数的对比测试，验证了该改进型量子遗传算法的可行性和有效性。
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| 关 键 词： | 量子遗传算法 浓度 改进型量子遗传算法 对比测试 |
| 收稿时间： | 2015-12-10 |
An Improved Quantum Genetic Algorithm Based on Concentration Adjusting |
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| HU Xiao-xiang and LIU Man-dan. An Improved Quantum Genetic Algorithm Based on Concentration Adjusting[J]. Researches in Medical Education, 2016, 0(5): 690-695 |
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| Authors: | HU Xiao-xiang and LIU Man-dan |
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| Affiliation: | Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China and Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | There are shortcomings of slow convergence and easily falling into local optimum using quantum genetic algorithm (QGA) to optimize multimodal functions.This paper proposes an improved quantum genetic algorithm (IQGA) by introducing the concept of concentration.Before updating quantum gates,IQGA screens and culls the individuals of high concentrations and inferior individuals,and then utilizes new individuals to replace them so as to improve the global search capability.The comparison test among five typical complex continuous functionst verifies the feasibility and effectiveness of the proposed IQGA. |
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| Keywords: | quantum genetic algorithm concentration improved quantum genetic algorithm comparison test |
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