The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.
设计了一种新型的高带宽、多频段单极子微带天线。以单极子天线为基础,通过在主天线旁边增加寄生的短路共面贴片,改变各种自然模式的场分布而产生多频,同时增加带宽。利用电磁仿真软件HFSS建立天线模型并优化仿真,结果表明该天线实现了WLAN(Wireless Local Area Network)2.45、5.2、5.8 GHz以及WiMAX(World Interoperability for Microwave Access)2.59、3.55、5.5 GHz多频段同时工作,甚至覆盖X频段。
