Improvement of adaptive genetic algorithms and application in line simplification

Abstract

Abstract: Fixed genetic probabilities easily cause the premature and slow convergence problem. Improvement of current adaptive genetic algorithm is proposed to avoid that. Through the experiments of optimization for common test functions, this improved algorithm shows its better global optimal ability and faster convergence ability. Based on this, this improved algorithm is applied in line simplification. Simulation results show that it can maintain the overall shape, and can get better simplification results.

Key words: adaptive genetic algorithm, improvement, line simplification

摘要： 针对固定遗传概率容易引起早熟及收敛慢的问题，对现有自适应遗传算法进行了改进。通过常见测试函数优化求解试验，验证了改进算法具有更好的全局收敛性和更快的收敛速度。在此基础上，将改进算法应用于曲线化简。仿真试验表明，其不仅能够较好地保持曲线的整体形态，还能够得到形变误差更小的化简结果。

关键词: 自适应遗传算法, 改进, 曲线化简

REN Haiyan, CHEN Feixiang. Improvement of adaptive genetic algorithms and application in line simplification[J]. Computer Engineering and Applications, 2012, 48(11): 152-155.

任海艳，陈飞翔. 自适应遗传算法的改进及在曲线化简中的应用[J]. 计算机工程与应用, 2012, 48(11): 152-155.

[1]	CHEN Qianru, LI Yali, XU Kequan, LIU Yilong, WANG Shuqin. WKNN Feature Selection Method Based on Self-Tuning Adaptive Genetic Algorithm [J]. Computer Engineering and Applications, 2021, 57(20): 164-171.
[2]	DAI Jiangtao, HAN Xiaolong. Coordinated Scheduling of Equipment in Container Terminals Considering Energy Consumption Under Different Job Status [J]. Computer Engineering and Applications, 2021, 57(19): 290-298.
[3]	QIU Yunfei, GAO Huacong. Hybrid Filter and Improved Adaptive GA for Feature Selection [J]. Computer Engineering and Applications, 2021, 57(11): 95-102.
[4]	XU Li, LIU Yunhua, WANG Qifu. Application of Adaptive Genetic Algorithm in Robot Path Planning [J]. Computer Engineering and Applications, 2020, 56(18): 36-41.
[5]	BAI Chuang, CHEN Xiang. Research on New LeNet-FC Convolutional Neural Network Model Algorithm [J]. Computer Engineering and Applications, 2019, 55(5): 105-111.
[6]	LUO Xiong, QIAN Qian, FU Yunfa. Review of Application of Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems [J]. Computer Engineering and Applications, 2019, 55(23): 15-21.
[7]	WANG Keqin, WU Fengjun. Online Reviews Mining for Product Design Improvement [J]. Computer Engineering and Applications, 2019, 55(19): 235-245.
[8]	XU Degang, ZHAO Ping. Literature Survey on Research and Application of Bat Algorithm [J]. Computer Engineering and Applications, 2019, 55(15): 1-12.
[9]	XIA Yichong, CHEN Qiulian, SONG Renkun. Packing of rectangles using adaptive genetic simulated annealing algorithm [J]. Computer Engineering and Applications, 2018, 54(22): 229-232.
[10]	HAN Xiaolong, LI Shang, YANG Quanye. Strategic supply chain integration research based on genetic algorithm [J]. Computer Engineering and Applications, 2018, 54(2): 214-221.
[11]	YANG Weibo1，2, WANG Wanliang1, ZHANG Jingling3, ZHAO Yanwei3. Packing optimization of rectangles based on improved genetic annealing algorithm [J]. Computer Engineering and Applications, 2016, 52(7): 259-263.
[12]	CAO Jie1，2, ZHANG Ling1. Multi-Agent traffic signal control based on adaptive genetic algorithm [J]. Computer Engineering and Applications, 2016, 52(13): 265-270.
[13]	ZHANG Wei, FAN Nianbai, WANG Wenjia. Stock prediction model research based on improved adaptive genetic algorithm [J]. Computer Engineering and Applications, 2015, 51(4): 254-259.
[14]	PENG Lirong1, ZHOU Lei2. Fusion of DM with cognitive and behavior calculating models for improving teaching quality [J]. Computer Engineering and Applications, 2014, 50(9): 237-241.
[15]	LI Huaying, LIU Jianmin, QIAO Xinyong, LI Xiaolei. Method research of fault feature extraction based on AGA-RS [J]. Computer Engineering and Applications, 2014, 50(5): 257-260.

Improvement of adaptive genetic algorithms and application in line simplification

自适应遗传算法的改进及在曲线化简中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics