[1]马龙,卢才武,顾清华.求解离散优化问题的元胞量子狼群演化算法[J].智能系统学报,2018,13(5):716-727.[doi:10.11992/tis.201705007]
MA Long,LU Caiwu,GU Qinghua.Cellular and quantum-behaved wolf pack evolutionary algorithm for solving discrete optimization problems[J].CAAI Transactions on Intelligent Systems,2018,13(5):716-727.[doi:10.11992/tis.201705007]
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MA Long,LU Caiwu,GU Qinghua.Cellular and quantum-behaved wolf pack evolutionary algorithm for solving discrete optimization problems[J].CAAI Transactions on Intelligent Systems,2018,13(5):716-727.[doi:10.11992/tis.201705007]
求解离散优化问题的元胞量子狼群演化算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第5期
页码:
716-727
栏目:
学术论文—机器学习
出版日期:
2018-09-05
- Title:
- Cellular and quantum-behaved wolf pack evolutionary algorithm for solving discrete optimization problems
- Keywords:
- discrete optimization; quantum-inspired wolf pack algorithm; cellular automata; double strategy method; sliding mode crossover; binary encoding; functional analysis; wolf pack algorithm; quantum rotation angle
- 分类号:
- TP301.6
- 摘要:
- 针对离散空间优化问题，提出了求解离散优化问题的元胞量子狼群演化算法，首先，为了提高算法的全局收敛速度，采用双策略量子位初始化方法和滑模交叉方法，分别生成量子狼群初始位置和产生头狼，实现种群多样性；其次，为了描述头狼与猎物间的距离以及增强狼群的遍历范围，采用二进制编码方式和元胞自动机中的演化规则，分别实现狼群中个体狼与猎物距离的精确描述和量子旋转角的选取调整；然后，为了证明该算法的收敛性能，采用泛函分析方法，实现了算法全局收敛性能的验证；最后，通过6个标准测试函数的仿真实验，并与狼群算法以及量子狼群算法的优化结果进行比较。实验结果表明，该算法具有较快的收敛速度和较好的全局寻优能力。
- Abstract:
- To solve optimization problems in discrete space, a cellular quantum-inspired wolf pack evolutionary algorithm is proposed for solving discrete optimization problems. First, to speed up the global convergence of the algorithm, when generating the diversity of population, the method fully utilizes the double strategy quantum bit initialization method and the sliding mode crossover method to help generate the initial position and candidate wolf, respectively. Then, to accurately describe the distance between the wolf and the prey as well as enhance the traverse range of wolf pack, the methods of the binary encoding and evolution rules of the cellular automata are used to realize precise description and the selection of the quantum rotation angle, respectively. Then to prove the convergence performance of the algorithm, the method fully utilizes the functional analysis to verify the global convergence. Finally, simulation experiment on six benchmark functions was carried out, and the comparison between the wolf pack algorithm and quantum-inspired wolf pack evolutionary algorithm was provided. The results show that the proposed approach has better convergence speed and great global convergence optimization ability.
- 参考文献/References:
-
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备注/Memo
收稿日期:2017-05-08。
基金项目:国家自然科学基金项目（51774228，51404182）；陕西省自然科学基金项目（2017JM5043）；陕西省教育厅专项科研计划项目（17JK0425）.
作者简介:马龙,男,1982年生,讲师,博士研究生,主要研究方向为群智能算法、系统建模与优化。参与国家自然科学基金青年项目1项、国家自然科学基金面上项目1项、陕西省自然科学基金项目1项、陕西省教育厅专项科研计划项目1项,横向项目1项。获得专利4项。发表学术论文9篇;卢才武,男,1965年生,教授,博士,主要研究方向为系统优化理论、信息技术和信息管理。主持省部级项目多项、横向项目多项。获中国工业大奖提名奖1项、中国有色科技进步二等奖1项,授权软件著作权多项,发明专利多项。发表学术论文80余篇,出版学术著作2部;顾清华,男,1981年生,副教授,博士,主要研究方向为系统优化理论、矿业系统工程优化。主持国家自然科学基金2项,省部级项目多项、横向项目多项。获得洛阳市科技进步一等奖1项,西安市科技进步二等奖1项,授权软件著作权多项、发明专利多项。发表学术论文30余篇。
通讯作者:顾清华.E-mail:qinghuagu@126.com.
更新日期/Last Update:
2018-10-25