关键词: 杂草优化算法, 混合混沌系统, 多等级子群, 差分进化模型

Abstract: Aiming at addressing the problems of standard invasive weed optimization algorithm such as premature convergence, slow convergence rate and easily falling into local optimum, a multi-level sub-population Invasive Weed Optimization Algorithm with New Differential Evolution Model（DEMIWO） is proposed. Firstly, an improved mixed chaotic system is adopted to increase the diversity of the initial population. Secondly, a new grouping strategy of different levels is proposed, which divides the population into four subgroups according to fitness. Meanwhile, a new differential evolution model is proposed by dynamically adjusting the crossover probability and mutation probability in the model. In the end, simulation experiment on 8 benchmark test functions shows that the proposed algorithm not only has faster convergence speed and higher convergence accuracy, but also can avoid falling into local optimum effectively compared with the standard IWO algorithm and other optimization algorithms.

Key words: invasive weed optimization, mixed chaotic system, multi-level sub-population, differential evolution model