中文说明：
提出了一种新颖的求解约束问题的群智能优化算法。该算法模拟杂草 克隆、占地生长与繁殖的自然行为，具有入侵性杂草的鲁棒性、适应性和随机性等特点，算法简单而有效，具有准确的全局搜索能力。结合罚函数方法将提出的算法 应用于求解工程设计优化问题,实验结果及比较表明提出的算法获得了更优的结果，同时也显示了它在求解复杂工程设计优化问题时的全局寻优能力。进一步实验与 统计分析了关于参数选择对算法性能的影响,得到了有利参数选择的结论。
English Description：
Using invasive weed optimization algorithm to solve two-dimensional function optimization problem.A novel swarm intelligence optimization algorithm for constrained problems is proposed. The algorithm simulates the natural behavior of weed cloning, growth and reproduction, and has the characteristics of robustness, adaptability and randomness of invasive weeds. The algorithm is simple and effective, and has accurate global search ability. Combined with the penalty function method, the proposed algorithm is applied to solve engineering design optimization problems. Experimental results and comparison show that the proposed algorithm obtains better results, and also shows its global optimization ability in solving complex engineering design optimization problems. Further experiments and statistical analysis on the influence of parameter selection on the performance of the algorithm are carried out, and the conclusion of favorable parameter selection is obtained.