基于模拟退火粒子群算法的认知引擎研究

摘要/Abstract

摘要：

认知引擎的基本功能之一就是根据复杂多变的无线环境及业务需求，利用多目标优化策略，自适应地调整无线参数，实现动态环境下的可靠通信。目前，很多研究的重点集中在遗传算法（GA）及其改进算法上，但其收敛速度较慢，不利于复杂多变以及实时性要求较高的系统。对此，提出一种模拟退火粒子群算法（SABPSO），它采用模拟退火与粒子群算法交替迭代的方式，协同寻优。其可有效提高收敛速度，并克服基本粒子群算法易于陷入局部极值的缺点，增强全局寻优能力。最后，在不同通信模式下，利用多载波系统进行仿真，结果表明，SABPSO算法在收敛速度和平均适应度上优于基本算法。

关键词: 认知引擎, 多目标优化, 粒子群, 模拟退火

Abstract:

One of the basic functions of cognitive engine is to adjust adaptive wireless parameters and use multi-objective optimization strategies to achieve reliable communication in a dynamic environment according to complex wireless environments and service needs. Currently many studies focus on the genetic algorithm (GA) and its improvement, however, its slow convergence is not conducive to complex and high real-time systems. We propose an algorithm, called simulated annealing particle swarm optimization (SABPSO), which combines the simulated annealing algorithm and the particle swarm algorithm to search jointly for a better solution in an alternately iterative manner. It can effectively improve the convergence speed and overcome PSO's shortage of falling into local extreme values easily, thus enhancing the ability of global optimization. Finally, multi-carrier system simulation results in different communication scenarios show that the SABPSO outperforms the basic algorithms in convergence rate and average fitness.

Key words: cognitive engine, multi-objective optimization, particle swarm, simulated annealing

薛蒙蒙，马永涛，刘敬浩. 基于模拟退火粒子群算法的认知引擎研究[J]. 计算机工程与科学.

XUE Meng-meng，MA Yong-tao，LIU Jing-hao. Cognitive engine research based on simulated annealing and particle swarm optimization algorithm [J]. Computer Engineering & Science.

参考文献

［1］Kolodzy P. Spectrum policy task force report ［J］. Federal Communications Commission Tech rep rep et Docket,2002,40(4):147-158.
［2］Mitola J,Maguire G Q.Cognitive radio:Making software radios more personal［J］.Personal Communications IEEE,1999,6(4):13-18.
［3］Haykin S.Cognitive radio:Brain-empowered wireless communications［J］.IEEE Journal on Selected Areas in Communications,2005,23(2):201-220.
［4］Clancy C, Hecker J, Stuntebeck E.Applications of machine learning to cognitive radio networks［J］.IEEE Wireless Communications Magazine,2007,14(4):47-52.
［5］He A,Bae K K,Newman T R,et al. A saurvey of artificial intelligence for cognitive radios［J］.IEEE Transactions on Vehicular Technology,2010,59(4):1578-1592.
［6］Kennedy J,Eberhart R C.A discrete binary version of the particle swarm algorithm［C］∥Proc of the Conference on Systems Man & Cybernetics,1997:4104-4108.
［7］Pradhan P M,Panda G.Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine:A survey［J］.Ad Hoc Networks,2014,17(3):129-146.
［8］Tan X,Zhang H,Hu J.A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database［J］.Annals of Telecommunications-Annales des Télécommunications,2014,69(11-12):593-605.
［9］Shen M,Zhan Z H,Chen W N,et al.Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks［J］.IEEE Transactions on Industrial Electronics,2014,61(12):7141-7151.
［10］Zhan Z H, Zhang J, Li Y, et al. Adaptive particle swarm optimization［J］.IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics,2009,39(6):1362-1381.
［11］Ratnaweera A,Halgamuge S,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.
［12］Zhan Zhi-hui,Zhang J,Li Y,et al.Orthogonal learning particle swarm optimization.［J］.IEEE Transactions on Evolutionary Computation,2009,15(6):1763-1764.
［13］Juang Chia-Feng.A hybrid of genetic algorithm and particle swarm optimization for recurrent network design［J］.IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics,2004,34(2):997-1006.
［14］Zhao Zhi-jin,Zhang Wei-wei,Peng Zhen,et al.Cognitive decision engine based on co-evolutionary particle swarm optimization［J］.Computer Engineering,2011,37(3):163-165.(in Chinese)
［15］Yu Yang,Tan Xue-zhi,Yin Cong,et al.Cognitive decision engine based on binary chaotic particle swarm optimization［J］.Journal of Harbin Institute of Technology,2014,46(3):8-13.(in Chinese)
［16］Andrews P S.An investigation into mutation operators for particle swarm optimization［C］∥Proc of IEEE Congress on Evolutionary Computation,2006:1044-1051.
［17］Wang Wei,Yin Zhi-xiang.Escapable particle swarm optimization based on simulated annealing algorithm［J］.Application Research of Computers,2008,25(5):1326-1339.(in Chinese)
［18］Yu Y,Tan X,Zou Q,et al.Research on cognitive making decision engine［C］∥Proc of the 3rd International Conference on Instrumentation,Measurement,Computer,Communication and Control,2013:125-128.
［19］Kaur K, Rattan M, Patterh M S. Optimization of cognitive radio system using simulated annealing［J］.Wireless Personal Communications,2013,71(2):1283-1296.
［20］Liu Ai-jun,Yang Yu,Li Fei,et al.Chaotic simulated annealing particle swarm optimization algorithm research and its application［J］.Journal of Zhejiang University(Engineering Science),2013,47(10):1722-1730.(in Chinese)
［21］Wang Zhen-shu,Li Lin-chuan,Li Bo.Reactive power optimization based on particle swarm optimization and simulated annealing cooperative algorithm［J］.Journal of Shandong University(Engineering Science),2008,38(6):15-20.(in Chinese)
附中文参考文献：
［14］赵知劲,张伟卫,彭振,等.基于协进化粒子群优化的认知决策引擎［J］.计算机工程,2011,37(3):163-165.
［15］于洋,谭学治,殷聪,等 .基于二进制混沌粒子群算法的认知决策引擎［J］.哈尔滨工业大学学报，2014,46(3):8-13.
［17］王伟,殷志祥.基于模拟退火算法的可逃逸粒子群算法［J］.计算机应用研究,2008,25(5):1326-1339.
［20］刘爱军,杨育,李斐,等.混沌模拟退火粒子群优化算法研究及应用［J］.浙江大学学报（工学版）,2013,47(10):1722-1730.
［21］王振树,李林川,李波.基于粒子群与模拟退火相结合的无功优化算法［J］.山东大学学报（工学版）,2008,38(6):15-20.

[1]	罗莉, 石伟, 何鸿君, 潘国腾, 王蕾, 龚锐. 一种面向IO Die的敏捷验证方法[J]. 计算机工程与科学, 2023, 45(04): 571-576.
[2]	彭坤彦, 尹翔, 刘笑竹, 李恒宇. 基于粒子群优化和深度强化学习的策略搜索方法[J]. 计算机工程与科学, 2023, 45(04): 718-725.
[3]	申晓宁, 游璇, 陈庆洲, 潘红丽, 黄遥. 采用双档案协同进化离散多目标烟花算法的低碳疫苗冷链优化配送[J]. 计算机工程与科学, 2022, 44(12): 2255-2265.
[4]	王林, 王燕丽, 安泽远. 改进粒子群算法优化回声状态网络的电力需求预测研究[J]. 计算机工程与科学, 2022, 44(08): 1457-1466.
[5]	申晓宁, 潘红丽, 陈庆洲, 游璇, 黄遥. 引入启发信息的粒子群算法在低碳TSP中的应用[J]. 计算机工程与科学, 2022, 44(06): 1114-1125.
[6]	赵炳巍, 贾峰, 曹岩, 孙瑜, 刘一鸿. 基于模拟退火算法的人工势场法路径规划研究[J]. 计算机工程与科学, 2022, 44(04): 746-752.
[7]	张晶, 魏淼, . 基于Delaunay三角划分策略的WSN区域覆盖优化研究[J]. 计算机工程与科学, 2021, 43(11): 1944-1951.
[8]	袁也, 王刚, 刘晓光, 李雨森. 新型电路版图布局布线算法设计[J]. 计算机工程与科学, 2021, 43(07): 1185-1191.
[9]	李艺辉, 刘作军, 李洁. 基于模糊逻辑NSGA-Ⅲ的开关磁阻发电机多目标优化算法[J]. 计算机工程与科学, 2021, 43(03): 542-550.
[10]	张明珠, 曹杰, 王斌. 基于精英集的多目标差分进化聚类算法[J]. 计算机工程与科学, 2021, 43(01): 170-179.
[11]	王翼虎, 王思明. 基于改进粒子群算法的无人机路径规划[J]. 计算机工程与科学, 2020, 42(09): 1690-1696.
[12]	马永杰, 陈满丽, 陈敏. 基于Pareto解集分段预测策略的动态多目标进化算法[J]. 计算机工程与科学, 2020, 42(08): 1454-1462.
[13]	高海军, 潘大志. 星型结构的多目标粒子群算法求解多模态多目标问题[J]. 计算机工程与科学, 2020, 42(08): 1472-1481.
[14]	王永琦, 江潇潇. 基于混合灰狼算法的机器人路径规划[J]. 计算机工程与科学, 2020, 42(07): 1294-1301.
[15]	衡红军, 戚馨桐. 考虑任务均衡的加油车动态调度问题[J]. 计算机工程与科学, 2020, 42(05): 923-930.