An improved hybrid invasive weed optimization for antenna beamformer

To improve the precision and convergence rate of antenna array beamformer, an improved hybrid invasive weed optimization (IHIWO) is proposed in this paper. As regards to the innovation of the IHIWO algorithm, the distribution variance is improved instead of the exponential function used in the original invasive weed optimization (IWO). Invoking the adopted method, the relationship between the global convergence and the local convergence can be effectively balanced. Simulations are performed based on rastrigin and grienwank functions to demonstrate the effectiveness of the proposed method as compared with IWO, Modified Adaptive Dispersion Invasive Weed Optimization (MADIWO) and IHIWO. The viability of the employed algorithm is also illustrated with numerical simulations in the application of antenna array beamformer. Simulation results show that the IHIWO algorithm can not only guarantee the higher convergence accuracy, but also improve the convergence rate, when compared with the IWO and ADIWO methods. Furthermore, the presented algorithm also holds the strong ability against the interferences, and it does not incline to falling into the local optimal solution.