针对天线优化设计通常涉及高度非线性的问题,传统优化算法往往无法获得全局最优解,在此研究背景下,引入文化粒子群优化算法(Cultural Based PSO Algorithm,CBPSO);针对高频电磁仿真软件(HFSS)仿真计算量大、耗时长的问题,引入Kriging模...针对天线优化设计通常涉及高度非线性的问题,传统优化算法往往无法获得全局最优解,在此研究背景下,引入文化粒子群优化算法(Cultural Based PSO Algorithm,CBPSO);针对高频电磁仿真软件(HFSS)仿真计算量大、耗时长的问题,引入Kriging模型替代费时的仿真计算,并通过动态更新的方法提高模型精度,提出了基于动态Kriging模型的文化粒子群算法与HFSS联合仿真优化设计方案。将该方案应用于WLAN/Wi MAX多频带天线优化设计,测试结果表明,所设计的天线在2.4~3.0GHz、3.3~3.8GHz、5.1~6.0GHz频段内回波损耗小于-10d B,覆盖了WLAN/Wi MAX所有频段,为复杂天线结构的优化设计提供了一定的参考。展开更多
Binary particle swarm optimization algorithm(BPSOA) has the excellent characters such as easy to implement and few set parameters.But it is tendentious to stick in the local optimal solutions and has slow convergence ...Binary particle swarm optimization algorithm(BPSOA) has the excellent characters such as easy to implement and few set parameters.But it is tendentious to stick in the local optimal solutions and has slow convergence rate when the problem is complex.Cultural algorithm(CA) can exploit knowledge extracted during the search to improve the performance of an evolutionary algorithm and show higher intelligence in treating complicated problems.So it is proposed that integrating binary particle swarm algorithm into cultural algorithm frame to develop a more efficient cultural binary particle swarm algorithm (CBPSOA) for fault feature selection.In CBPSOA,BPSOA is used as the population space of CA;the evolution of belief space adopts crossover,mutation and selection operations;the designs of acceptance function and influence function are improved according to the evolution character of BPSOA.The tests of optimizing functions show the proposed algorithm is valid and effective.Finally,CBPSOA is applied for fault feature selection.The simulations on Tennessee Eastman process (TEP) show the CBPSOA can perform better and more quickly converge than initial BPSOA.And with fault feature selection,more satisfied performance of fault diagnosis is obtained.展开更多
基金National High Technology Research and Development Program of China(No.2007AA04Z171)
文摘Binary particle swarm optimization algorithm(BPSOA) has the excellent characters such as easy to implement and few set parameters.But it is tendentious to stick in the local optimal solutions and has slow convergence rate when the problem is complex.Cultural algorithm(CA) can exploit knowledge extracted during the search to improve the performance of an evolutionary algorithm and show higher intelligence in treating complicated problems.So it is proposed that integrating binary particle swarm algorithm into cultural algorithm frame to develop a more efficient cultural binary particle swarm algorithm (CBPSOA) for fault feature selection.In CBPSOA,BPSOA is used as the population space of CA;the evolution of belief space adopts crossover,mutation and selection operations;the designs of acceptance function and influence function are improved according to the evolution character of BPSOA.The tests of optimizing functions show the proposed algorithm is valid and effective.Finally,CBPSOA is applied for fault feature selection.The simulations on Tennessee Eastman process (TEP) show the CBPSOA can perform better and more quickly converge than initial BPSOA.And with fault feature selection,more satisfied performance of fault diagnosis is obtained.
