[1]陈静静,刘 升.基于禁忌搜索的自适应人工鱼群优化算法[J].计算机技术与发展,2021,31(03):8-13.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 002]
　CHEN Jing-jing,LIU Sheng.An Adaptive Artificial Fish Swarm Optimization Algorithm Based on Taboo Search[J].,2021,31(03):8-13.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 002]
点击复制

基于禁忌搜索的自适应人工鱼群优化算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年03期
页码:
8-13
栏目:
人工智能
出版日期:
2021-03-10

文章信息/Info

Title:
An Adaptive Artificial Fish Swarm Optimization Algorithm Based on Taboo Search
文章编号:
1673-629X(2021)03-0008-06
作者:
陈静静; 刘 升
上海工程技术大学,上海 201620
Author(s):
CHEN Jing-jing; LIU Sheng
Shanghai University of Engineering Science,Shanghai 201620,China
关键词:
人工鱼群算法; 分段函数; 正态分布函数; 自适应; levy 飞行; 禁忌搜索
Keywords:
artificial fish swarm algorithm; piecewise function; normal distribution function; adaptive; levy flight; Taboo search
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 03. 002
摘要:
针对人工鱼群算法在函数优化过程中存在易陷入局部最优、后期收敛速度慢和寻优精度低等问题,提出了一种基于禁忌搜索的自适应人工鱼群优化算法。 由于较大的视野范围有利于进行全局探索,较小的视野范围有助于进行局部寻优,该算法引入了分段函数自适应地调整视野,保证了视域在一定范围内随着迭代的进行逐渐减小;利用正态分布函数以及鱼群间距的大小对步长进行了改进,来协调寻优速度与解精度之间的平衡;为了更加贴合生物觅食的本能,在随机行为中加入了具有 levy 飞行机制的自由游动算子,不仅加强了鱼的全局搜索能力,还降低了随机行为因盲目性而导致解退化的风险;为了改善鱼群因陷入局部极值而出现寻优停滞不前的状况,引入了禁忌搜索思想。 实验结果表明,改进后的算法具有明显的寻优优势。
Abstract:
In the process of function optimization, artificial fish swarm algorithm has many problems, such as easy to? ?fall into local optimum,slow convergence speed and low optimization precision,an adaptive artificial fish swarm optimization algorithm based on Taboo search is proposed. Since a larger field of view is conducive? ? to global exploration and a smaller field of view is helpful for local optimization,the algorithm introduces a piecewise function to adaptively adjust the field of view,which ensures that the field of view gradually decreases in a certain range with the progress of iteration. The step size is improved by using the normal distribution function and the distance of the fish to coordinate the balance between optimization speed and solution accuracy. In order to fit the nature of biological foraging,a free swimming operator with a levy flight mechanism is added to the random behavior,which not only strengthens the global search ability but also reduces the risk of random behavior causing solution degradation due. In order to improve the situation that fish get stuck in the local extremum,the idea of Taboo search is introduced. The experiment shows that the improved algorithm has obvious advantages in optimization

相似文献/References:

[1]王会颖 章义刚.求解聚类问题的改进人工鱼群算法[J].计算机技术与发展,2010,(03):84.
　WANG Hui-ying,ZHANG Yi-gang.An Improved Artificial Fish- Swarm Algorithm of Solving Clustering Analysis Problem[J].,2010,(03):84.
[2]古明家 宣士斌 廉侃超 李永胜.基于蚁群和人工鱼群算法融合的QoS路由算法[J].计算机技术与发展,2009,(07):145.
　GU Ming-jia,XUAN Shi-bin,LIAN Kan-chao,et al.QoS Routing Algorithm Based on Combination of Modified Ant Colony Algorithm and Artificial Fish Swarm Algorithm[J].,2009,(03):145.
[3]史学军,方金鑫,于舒娟.基于全局人工鱼群算法的盲均衡[J].计算机技术与发展,2013,(05):75.
　SHI Xue-jun,FANG Jin-xin,YU Shu-juan.Blind Equalization Based on Global Artificial Fish Swarm Algorithm[J].,2013,(03):75.
[4]刘晓丽,熊良鹏. 改进的人工鱼群算法在机器人控制中的应用[J].计算机技术与发展,2015,25(05):200.
　LIU Xiao-li,XIONG Liang-peng. Application of Robot Control Using Improved Artificial Fish Swarm Algorithm[J].,2015,25(03):200.
[5]彭培真,俞毅,王兆嘉,等. 基于单纯形的改进全局人工鱼群优化算法[J].计算机技术与发展,2015,25(08):75.
　PENG Pei-zhen,YU Yi,WANG Zhao-jia,et al. Improved Global Artificial Fish Swarm Algorithm Based on Simplex Method[J].,2015,25(03):75.
[6]汪晨,张玲华.基于人工鱼群算法的改进质心定位算法[J].计算机技术与发展,2018,28(05):103.[doi:10.3969/ j. issn.1673-629X.2018.05.024]
　WANG Chen,ZHANG Ling-hua.An Improved Centroid Localization Algorithm Based on Optimized Artificial Fish Swarm Algorithm[J].,2018,28(03):103.[doi:10.3969/ j. issn.1673-629X.2018.05.024]
[7]秦军[],翟钊[]. 基于Hadoop MapReduce的组合服务性能优化研究[J].计算机技术与发展,2016,26(05):61.
　QIN Jun[],ZHAI Zhao[]. Research on Composite Service Performance Optimization Based on Hadoop MapReduce[J].,2016,26(03):61.
[8]唐莉[],张正军[],王俐莉[]. 人工鱼群算法的改进[J].计算机技术与发展,2016,26(11):37.
　TANG Li[],ZHANG Zheng-jun[],WANG Li-l. Improvement of Artificial Fish Swarm Algorithm[J].,2016,26(03):37.
[9]陈亚[],李萍[]. 人工鱼群神经网络在短期负荷预测中的应用[J].计算机技术与发展,2017,27(10):189.
　CHEN Ya[],LI Ping[]. Application of Artificial Fish Swarm Neural Network in Short Term Load Forecasting[J].,2017,27(03):189.
[10]徐胜超,杨 波.基于人工鱼群算法的容器云资源低能耗部署方法[J].计算机技术与发展,2023,33(06):22.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 004]
　XU Sheng-chao,YANG Bo.Low Energy Consumption Deployment Method for Container Cloud Resources Based on Artificial Fish Swarm Algorithm[J].,2023,33(03):22.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 004]

更新日期/Last Update: 2020-03-10