JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2014, Vol. 44 ›› Issue (6): 1-7.doi: 10.6040/j.issn.1672-3961.2.2014.306

    Next Articles

Improved genetic algorithm based on the dual-mode mutation strategy

LIANG Xingjian1,2, ZHAN Zhihui3   

  1. 1. School of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, Sichuan, China;
    2. Key Lab of Enterprise Informationization and Internet of Things of Sichuan Province, Zigong 643000, Sichuan, China;
    3. Department of Computer Science, Sun Yat-sen University, Guangzhou 510006, Guangdong, China
  • Received:2014-03-26 Revised:2014-10-15 Online:2014-12-20 Published:2014-03-26

Abstract: Aiming at the defects in the standard genetic algorithm such as slow optimization speed and local optimum, an improved genetic algorithm based on the Dual-Mode Mutation strategy is put forward. On the basis of the standard mutation, the idea of individual linear difference mutation is introduced to form the Dual-Mode Mutation balanced by the controlling parameters. The results of simulation experiments on 10 benchmarking functions shows that this algorithm can greatly improve the optimization speed and global convergence and has application value.

Key words: algorithm improvement, dual-mode mutation strategy, differential evolution, genetic algorithm, mutation operator of optimization

CLC Number: 

  • TP18
[1] ZHANG J, CHUNG H S, LO W L. Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(3):326-335.
[2] 龚月姣,陈梦君,胡晓敏,等.遗传算法中自适应方法的比较和分析[J].计算机工程与设计,2009,30(21):4903-4913. GONG Yuejiao, CHEN Mengjun, HU Xiaomin, et al. Comparison and analysis of adaptive genetic algorithms[J]. Computer Engineering and Design, 2009, 30(21):4903-4913.
[3] 王晓峰,随婷婷.基于TIGA_S4VM改进算法的蛋白质序列识别方法[J]. 山东大学学报:工学版,2014,44(1):1-6. WANG Xiaofeng, SUI Tingting. Protein sequence identifycation based on improved TIGA_S4VM algorithm[J]. Journal of Shandong University: Engineer Science, 2014, 44(1):1-6.
[4] OUERFELLI H, DAMMAK A. The Genetic Algorithm with two point crossover to solve the Resource-Constrained Project Scheduling Problems[C]//International Conference on Modeling, Simulation and Applied Optimization. Hammamet, Tunisia: IEEE, 2013:1-4.
[5] GAO Y, ZHENG T. Improved genetic algorithms based on chaotic mutation operation and its application[C]//International Conference on Multimedia Technology, Ningbo,China: IEEE, 2010:1-3.
[6] ABIDO M A, ELAZOUNI A. Improved Crossover and Mutation Operators for Genetic Algorithm Project Scheduling[C]//IEEE Congress on Evolutionary Computation. Trondheim,Norway: IEEE, 2009:1865-1872.
[7] 何涛,张洪伟,邹书蓉. 特征提取与多目标机器学习研究及应用[J]. 四川理工学院学报:自然科学版,2013, 26(1): 33-37. HE Tao, ZHANG Hongwei, ZOU Shurong. Research and Application of Feature Extraction and Multi-objective Machine Learning[J]. Journal of Sichuan University of Science & Engineering:Natural Science Edition, 2013, 26(1): 33-37.
[8] RITTHIPAKDEE A, THAMMANO A, PREMASATHIAN N, et al. A New Selection Operator to Improve the Performance of Genetic Algorithm for Optimization Problems[C]//IEEE ICMA Conference International Scientific Advisory Board. Takamatsu, Japan:IEEE, 2013:371-375.
[9] 丁若冰,邹书蓉. 基于聚类划分子种群的多种群遗传算法[J].四川理工学院学报:自然科学版,2014,27(3):1-4. DING Ruobing, ZOU Shurong. Multiple Populations Genetic Algorithm Based on Clustering Dividing Child Populations[J]. Journal of Sichuan University of Science & Engineering:Natural Science Edition, 2014, 27(3):1-4.
[10] 张琛,詹志辉.遗传算法选择策略比较[J].计算机工程与设计,2009,30(23):5471-5478. ZHANG Chen, ZHAN Zhihui Comparisons of Selection Strategy in Genetic Algorithm[J]. Computer Engineering and Design, 2009, 30(23):5471-5478.
[11] ZHONG J H, HU X M, GU M, et al. Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms[C]//International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce. Vienna,Austria: IEEE, 2005:1115-1121.
[12] RAJAKUMAR B R, GEORGE A. A New Adaptive Muta-tion Technique for Genetic Algorithm[C]//IEEE Interna-tional Conference on Computational Intelligence and Computing Research. Coimbatore India: IEEE, 2012:1-7.
[13] 段海滨,张祥银,徐春芳.仿生智能计算[M].北京:科学出版社,2011:108-114.
[1] CHEN Jiajie, WANG Jinfeng. Method for solving Choquet integral model based on ant colony algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 81-87.
[2] ZHANG Shuangsheng, QIANG Jing, LIU Xikun, LIU Hanhu, ZHU Xueqiang. Inverse problems of pollution source identification based on Bayesian-DE [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 131-136.
[3] WANG Fei, XU Jian, LI Wei, WANG Xinhao, SHI Xiaohan. Rolling optimal dispatch method of wind power based on distributed energy storage system [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(6): 89-94.
[4] DENG Guanlong, YANG Hongyong, ZHANG Shuning, GU Xingsheng. Multi-objective scheduling in no-wait flow shop using a hybridized differential evolution algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(5): 21-28.
[5] LIU Debao, WU Yaohua, GUO Yaoyang, WANG Yanyan. Item assignment optimization of automatic picking system based on hybrid picking strategy [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(6): 36-44.
[6] DONG Hongbin, ZHANG Guangjiang, PANG Jinwei, HAN Qilong. A clustering ensemble algorithm based on co-evolution [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(2): 1-9.
[7] YANG Longhao, FU Yanggeng, GONG Xiaoting. Parallel differential evolution algorithm for parameter learning of belief rule base [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(1): 30-36.
[8] SUN Peng, CHENG Shi-qing*, XIE Jing-si, ZHANG Hai-rui. CV-GA-SVM model for predicting the ash fusion point of a mixed biomass [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(2): 108-111.
[9] YANG Qinmin, LIU Hailin*. Dynamic channel allocation modeling and algorithm in cellular networks
based on a genetic algorithm
[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(2): 85-90.
[10] LIU Bin, ZHANG Ren-jin. NURBS curve approximation based on annealing genetic algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(5): 96-100.
[11] YANG Ai-min1, ZHOU Yong-mei1, DENG He2, ZHOU Jian-feng3. Method of feature generation and selection for network traffic classification [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(5): 1-7.
[12] WANG Yan-yan, WU Yao-hua, SUN Guo-hua, YU Hong-peng. Research on  picking  order  batching  policy  of  a  distribution  center [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(2): 43-46.
[13] GONG Dunwei, SUN Xiaoyan, REN Jie. Interactive genetic algorithms with tournament evaluation and evolutionary knowledge extraction [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 1-7.
[14] WANG Jian, ZHANG Shan. voltage  regulation research of an improved genetic algorithm considering infeasidering infeasibility degree [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(6): 21-24.
[15] LI Jie ,LIU Hong. A method of fractal artistic pattern generation based on a genetic algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(6): 33-36.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!