寻优能力增强型越界免疫粒子群算法
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摘要
PSO算法是提高WSN覆盖的一种全局优化算法。针对布尔感知模型与实际情况有所差别,且存在粒子搜索速度变慢的问题。提出了一种寻优能力增强型越界免疫粒子群算法(optimized ability enhancement and out of bounds immune PSO,OAEBI-PSO),采用概率感知模型,在粒子越界和粒子更新两方面做出了改进,得到了更高的覆盖率,并且避免陷入局部最优。仿真表明,该算法能够平均提高11%的覆盖率,并且通过50次的蒙特卡罗实验,表明该算法具有较强的稳定性。
PSO algorithm is a global optimization algorithm for improving WSN coverage. In view of the problem that the Boolean perception model is different from the actual situation and the particle search speed would be slow. An optimized ability enhancement and out of bounds immune PSO(OAEBI-PSO) are proposed, the probabilistic perception model is adopted and the algorithm in the particle bounds and the particle update are improved, as a consequence, the higher coverage ratio is obtained and local optima is avoided. Simulation results show that the algorithm can improve the average coverage by 11%, and the algorithm has a strong stability through the 50 times Monte-Carlo experiment.
PSO algorithm is a global optimization algorithm for improving WSN coverage. In view of the problem that the Boolean perception model is different from the actual situation and the particle search speed would be slow. An optimized ability enhancement and out of bounds immune PSO(OAEBI-PSO) are proposed, the probabilistic perception model is adopted and the algorithm in the particle bounds and the particle update are improved, as a consequence, the higher coverage ratio is obtained and local optima is avoided. Simulation results show that the algorithm can improve the average coverage by 11%, and the algorithm has a strong stability through the 50 times Monte-Carlo experiment.
引文
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[9] 冯琳, 冉晓旻, 孙韬. 逐维判断PSO算法值的WSN覆盖优化[J]. 计算机应用研究, 2015, 32(12): 3765-3768.
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[11] 周杰, 田敏, 钟福如. 基于混沌小生境狼群算法的高密度无线传感器网络高能效分簇方法[J]. 甘肃科技, 2016, 32(11): 38-40.
[12] 杨永建, 樊晓光, 甘轶,等. 基于改进 PSO 算法的传感器网络覆盖优化[J]. 系统工程与电子技术, 2017, 39(2): 310-315.
[13] 马羚, 李海军, 王成刚,等. 基于改进离散粒子群算法的传感器优化配置[J]. 电子学报, 2015, 43(12): 2408-2413.
[14] ZHANG W, MA D, WEI J J, et al. A parameter selection strategy for particle swarm optimization based on particle positions[J]. Expert Systems with Applications, 2014, 41(7): 3576-3584.
[15] ZHUO Z F, YANG Y J, Fan X G, et al. Array antennas pattern synthesis based on improved dichotomy particle swarm optimization[J]. Systems Engineering & Electronics, 2015,37(11):2460-2466.
[16] QIN L, ZHENG L, LIU Y F, et al. Maneuvering target collaborative tracking algorithm with multi-sensor deployment optimization[J]. Systems Engineering & Electronics, 2013, 35(2): 304-309.
[17] KUNDU R, DAS S, MUKHERJEE R, et al. An improved particle swarm optimizer with difference mean based perturbation[J]. Neurocomputing, 2014,129(129):315-333.
[18] PRATHABRAO M, NAWAWI A, SIDEK N A. Swarm size and iteration number effects to the performance of PSO algorithm in RFID tag coverage optimization[C]// International Conference on Mechanical & Manufacturing Engineering. AIP Publishing LLC, BeiJing,,China 2017:163-194.
[2] 王雪, 王晟, 马俊杰. 无线传感网络布局的虚拟力导向微粒群优化策略[J]. 电子学报, 2007, 35(11):2038-2042
[3] 高文华, 刘利民, 董增寿,等. 改进DPSO算法在数据采集系统信道选择中的应用[J]. 太原科技大学学报, 2017, 38(3):167-172.
[4] LU S,YU S Z. An improved particle swarm optimizer with attraction and repulsion[C]// Proc. of the 7th International Conference on Computing and Convergence Technology. IEEE, BeiJing,,China, 2013: 735-740
[5] WANG X, WSNG S, MA J. Dynamic Deployment Optimization in Wireless Sensor Networks[J]. Lecture Notes in Control & Information Sciences, 2006, 344: 182-187.
[6] 冯智博, 黄宏光, 李奕. 基于改进粒子群算法的WSN覆盖优化策略[J]. 计算机应用研究, 2011, 28(4): 1272-1275.
[7] SUN J, WU X, PALADE V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization[J]. Information Sciences, 2012, 193(15): 81-103.
[8] 禚真福, 杨永建, 樊晓光,等. 改进二分粒子群优化算法的阵列方向图综合[J]. 系统工程与电子技术, 2015, 37(11): 2460-2466.
[9] 冯琳, 冉晓旻, 孙韬. 逐维判断PSO算法值的WSN覆盖优化[J]. 计算机应用研究, 2015, 32(12): 3765-3768.
[10] LIU Z, ZHANG L, Department EE, et al. WSAN network coverage scheme based on SA-PSO algorithm[J]. Journal of Electronic Measurement & Instrumentation, 2016,30(7):1090-1097.
[11] 周杰, 田敏, 钟福如. 基于混沌小生境狼群算法的高密度无线传感器网络高能效分簇方法[J]. 甘肃科技, 2016, 32(11): 38-40.
[12] 杨永建, 樊晓光, 甘轶,等. 基于改进 PSO 算法的传感器网络覆盖优化[J]. 系统工程与电子技术, 2017, 39(2): 310-315.
[13] 马羚, 李海军, 王成刚,等. 基于改进离散粒子群算法的传感器优化配置[J]. 电子学报, 2015, 43(12): 2408-2413.
[14] ZHANG W, MA D, WEI J J, et al. A parameter selection strategy for particle swarm optimization based on particle positions[J]. Expert Systems with Applications, 2014, 41(7): 3576-3584.
[15] ZHUO Z F, YANG Y J, Fan X G, et al. Array antennas pattern synthesis based on improved dichotomy particle swarm optimization[J]. Systems Engineering & Electronics, 2015,37(11):2460-2466.
[16] QIN L, ZHENG L, LIU Y F, et al. Maneuvering target collaborative tracking algorithm with multi-sensor deployment optimization[J]. Systems Engineering & Electronics, 2013, 35(2): 304-309.
[17] KUNDU R, DAS S, MUKHERJEE R, et al. An improved particle swarm optimizer with difference mean based perturbation[J]. Neurocomputing, 2014,129(129):315-333.
[18] PRATHABRAO M, NAWAWI A, SIDEK N A. Swarm size and iteration number effects to the performance of PSO algorithm in RFID tag coverage optimization[C]// International Conference on Mechanical & Manufacturing Engineering. AIP Publishing LLC, BeiJing,,China 2017:163-194.