The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches.

入侵杂草算法（IWO）是一种元启发式算法，是解决优化问题的有效且有前途的优化器。在这项研究中，提出了一种基于自适应入侵杂草算法（IWO）和差分进化算法（DE）的混合算法，称为SIWODE，以解决连续优化问题。在所提出的SIWODE中，首先自适应地提出两个参数以提高算法的收敛速度。其次，SIWODE中引入了交叉和变异操作，以提高种群多样性，增加迭代过程中的探索能力。此外，提出了一种局部扰动策略，以提高后期过程中的开发能力。算法的探索和开发能力通过协作机制得到有效平衡。SIWODE 的实验结果表明，SIWODE 比其他方法具有更好的搜索质量和稳定性。