Convergence Analysis of Hybrid Free Search and Invasive Weed Optimization Algorithm

Abstract

Considering the fitness of each individual, a hybrid intelligence algorithm is established, which combine the excellent probing ability of free search algorithm (FS) with exploiting ability of invasive weed optimization algorithm (IWO). The hybrid algorithm can overcome the disadvantage of lower optimization rate in late evolution for FS and taking advantage of powerful exploiting abilities for IWO. Identity between FS and IWO is analyzed and convergence of the two algorithms in solving continuous function optimization is provided. Simulations confirmed the analysis. Multi-model Shubert function is chosen to carry out the simulation. Compared with FS and IWO, the hybrid algorithm is superior in convergence speed and robustness.