Computer Science ›› 2019, Vol. 46 ›› Issue (6): 95-101.doi: 10.11896/j.issn.1002-137X.2019.06.013

Previous Articles     Next Articles

Cognitive Decision Engine of Hybrid Learning Differential Evolution and Particle Swarm Optimization

ZHANG Yu-pei1, ZHAO Zhi-jin1,2, ZHENG Shi-lian2   

  1. (Zhejiang Provincial Key Lab of Data Storage and Transmission Technology of Hangzhou Dianzi University,Hangzhou 310018,China)1
    (State Key Lab of Information Control Technology in Communication System,No.36 Research Institute of China Electronic Technology Corporation,Jiaxing,Zhejiang 314001,China)2
  • Received:2018-05-27 Published:2019-06-24

Abstract: In order to increase the speed and performance of parameter decision in cognitive radio system,a cognitive radio decision engine (HPSO-BLDE) based on hybrid particle swarm optimization and learning differential evolution algorithm was proposed.First,the adaptive mutation mechanism is introduced into the learning differential evolution algorithm,so that each chromosome adaptively varies with individual fitness and average fitness to improve its local optimization capability.Then,the learning factor of particle swarm optimization algorithm is modified and the perturbation is added to prevent the premature.The more appropriate transform function is selected to convert the forward and backward velocity to the same probability to update the particle position and improve the precision of the optimal solution,thus improving the global optimization solution.Finally,the improved binary particle swarm optimization (IBPSO) and the improved binary differential evolution algorithm (IBLDE) are run in parallel in the cognitive engine model,and the best individual information of the two algorithms is fused after a fixed number of iterations to obtain the HPSO-BLDE algorithm.The populations of IBPSO algorithm and IBLDE algorithm have the both advantages,thus the optimal solution accuracy and convergence speed of the HPSO-BLDE algorithm are enhanced.Parameter decision simulation results of multi-carrier communication system shows that the IBPSO algorithm,IBLDE algorithm and HPSO-BLDE algorithm have better performance than hilling genetic algorithm (HGA),binary quantum particle swarm algorithm (BQPSO) and binary learningdifferential evolution algorithm (BLDE),and HPSO-BLDE algorithm has the best performance among these algorithms.

Key words: Cognitive radio, Cognitive decision engine, Reconfiguration, Differential evolution, Particle swarm optimization

CLC Number: 

  • TN929.5
[1]LUO Y Y,SUN Z F.Cognitive radio decision engine based on adaptive ant colony optimization[J].Computer Science,2011,38(8):253-256.(in Chinese)
罗云月,孙志峰.基于自适应蚁群优化算法的认知决策引擎[J].计算机科学,2011,38(8):253-256.
[2]YOU X,HE X,HAN X,et al.A novel cross-layer decision engine for cognitive radio based on quick-CQABC algorithm and channel gain information[J].Journal of Computational Information Systems,2015,11(14):5227-5242.
[3]PARASKEVOPOULOS A,DALLAS P I,SIAKAVARA K,et al. Cognitive radio engine design for IoT using real-coded bio-geography-based optimization and fuzzy decision making[J].Wireless Personal Communications,2017,97(2):1-21.
[4]CLANCY T C.Dynamic spectrum access using the interference temperature model[J].Annals of Telecommunications-annales Des Télé communications,2009,64(7-8):573-592.
[5]BKASSINY M,LI Y,JAYAWEERA S K.A survey on machine-learning techniques in cognitive radios[J].IEEE Communications Surveys & Tutorials,2013,15(3):1136-1159.
[6]DONG X,LI Y,WU C,et al.A learner based on neural network for cognitive radio[C]∥IEEE International Conference on Communication Technology.IEEE,Nanjing,China,2010:893-896.
[7]YIN L,YIN S X,HONG W,et al.Spectrum behavior learning in Cognitive Radio based on artificial neural network[C]∥2011- MILCOM,2011 Military Communications Conference.IEEE,Baltimore,MD,USA,2012.
[8]RIESER C J.Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking[M].Virginia Polytechnic Institute and State Universtity Press,2004.
[9]ZHAO Z J,ZHENG S L,SHANG J N,et al.A study of cognitive radio decision engine based on quantum genetic algorithm[J].Acta Physica Sinica,2007,56(11):6760-6766.(in Chinese)
赵知劲,郑仕链,尚俊娜,等.基于量子遗传算法的认知无线电决策引擎研究[J].物理学报,2007,56(11):6760-6766.
[10]ZHAO Z J,XU S Y,ZHENG S L,et al.Cognitive radio decision engine based on binary particle swarm optimization[J].Acta Physica Sinica,2009,58(7):5118-5125.(in Chinese)
赵知劲,徐世宇,郑仕链,等.基于二进制粒子群算法的认知无线电决策引擎[J].物理学报,2009,58(7):5118-5125.
[11]XU H,ZHOU Z.Hill-climbing genetic algorithm optimization in cognitive radio decision engine[C]∥IEEE International Confe-rence on Communication Technology.IEEE,Guilin,China,2014:115-119.
[12]ZHANG J,ZHOU Z,GAO W,et al.Cognitive radio adaptation decision engine based on binary quantum-behaved particle swarm optimization[C]∥International ICST Conference on Communications and Networking in China.IEEE,Harbin,China,2011:221-225.
[13]ISLAM M J,LI X,MEI Y.A Time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO[J].Applied Soft Computing,2017,20(5):1-43.
[14]SARHANI M,AFIA A E,FAIZI R,et al.Facing the feature selection problem with a binary PSO-GSA Approach[M]∥Recent Developments in Metaheuristics.Springer International Publi-shing.Rabat,Morocco,2018.
[15]CHEN Y,XIE W,ZOU X.A binary different evolution algo-rithm learning from explored solutions[J].Neurocomputing,2015,149:138-1047.
[16]YOU X,HE X,HAN X.A novel solution to the cognitive radio decision engine based on improved multi-objective artificial bee colony algorithm and fuzzy reasoning[J].Intelligent Automation &Soft Computing,2017,23(4):1-9.
[17]KAUR K,RATTAN M,PATTERH M S.Biogeography-based optimisation of Cognitive Radio system[J].International Journal of Electronics,2014,101(1):24-36.
[18]RATNAWEERA A,HALGAMUGE S K,WATSON H C.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J].IEEE Transactions on Evolutionary Computation,2004,8(3):240-255.
[19]CHENG R,JIN Y.A competitive swarm optimizer for large scale optimization[J].IEEE Transactions on Cybernetics,2015,45(2):191.
[1] ZHANG Zhi-qiang, LU Xiao-feng, SUI Lian-sheng, LI Jun-huai. Salp Swarm Algorithm with Random Inertia Weight and Differential Mutation Operator [J]. Computer Science, 2020, 47(8): 297-301.
[2] HOU Gai, HE Lang, HUANG Zhang-can, WANG Zhan-zhan, TAN Qing. Pyramid Evolution Strategy Based on Differential Evolution for Solving One-dimensional Cutting Stock Problem [J]. Computer Science, 2020, 47(7): 166-170.
[3] SUN Jun-yan, ZHANG Yuan-yuan, WU Bing-ying, NIU Ya-ru, CHEN Chan-juan. Evolution Analysis of Household Car Supply Chain Based on Multi-Agent [J]. Computer Science, 2020, 47(7): 171-178.
[4] QI Wei, YU Hui-qun, FAN Gui-sheng, CHEN Liang. WSN Coverage Optimization Based on Adaptive Particle Swarm Optimization [J]. Computer Science, 2020, 47(7): 243-249.
[5] SONG Yan, HU Rong-hua, GUO Fu-min, YUAN Xin-liang and XIONG Rui-yang. Improved SVM+BP Algorithm for Muscle Force Prediction Based on sEMG [J]. Computer Science, 2020, 47(6A): 75-78.
[6] ZHU Li-hua, WANG Ling, TANG Qi, WEI Ji-bo. Efficient MILP Model for HW/SW Partitioning of Dynamic Partial Reconfigurable SoC [J]. Computer Science, 2020, 47(4): 18-24.
[7] TIAN Miao-miao, WANG Zu-lin, XU Mai. DVB-S2 Signal Receiving and Analysis Based on Cognitive Radio [J]. Computer Science, 2020, 47(4): 226-232.
[8] LI Zhang-wei,WANG Liu-jing. Population Distribution-based Self-adaptive Differential Evolution Algorithm [J]. Computer Science, 2020, 47(2): 180-185.
[9] LI Bao-sheng, QIN Chuan-dong. Study on Electric Vehicle Price Prediction Based on PSO-SVM Multi-classification Method [J]. Computer Science, 2020, 47(11A): 421-424.
[10] WANG Xuan, MAO Ying-chi, XIE Zai-peng, HUANG Qian. Inference Task Offloading Strategy Based on Differential Evolution [J]. Computer Science, 2020, 47(10): 256-262.
[11] WANG Li-zhi,MU Xiao-dong,LIU Hong-lan. Using SVM Method Optimized by Improved Particle Swarm Optimization to Analyze Emotion of Chinese Text [J]. Computer Science, 2020, 47(1): 231-236.
[12] WANG Gai-yun, WANG Lei-yang, LU Hao-xiang. RSSI-based Centroid Localization Algorithm Optimized by Hybrid Swarm Intelligence Algorithm [J]. Computer Science, 2019, 46(9): 125-129.
[13] XUE Ling-ling, FAN Xiu-mei. Cognitive Spectrum Allocation Mechanism in Internet of Vehicles Based on Clustering Structure [J]. Computer Science, 2019, 46(9): 143-149.
[14] ZHANG Na,TENG Sai-na,WU Biao,BAO Xiao-an. Test Case Generation Method Based on Particle Swarm Optimization Algorithm [J]. Computer Science, 2019, 46(7): 146-150.
[15] DONG Ming-gang,LIU Bao,JING Chao. Multi-objective Differential Evolution Algorithm with Fuzzy Adaptive Ranking-based Mutation [J]. Computer Science, 2019, 46(7): 224-232.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .