Concerning the premature convergence of invasive weed optimization algorithm, a new invasive weed optimization with multi-population based on chaotic sequence (CMIWO) was proposed. Firstly, chaotic sequence was adopted to initialize population at the initialization of algorithm, which improved the quality of the initial solution. Secondly, threshold was used to estimate the cluster degree of individuals in iterations and if cluster degree was less than threshold, initializing population with chaotic sequence was implemented again, thus the algorithm could effectively jump out of local minima. Thirdly, the weed population was divided into five groups to collaborate so as to discourage premature convergence, thus improving the algorithm's precision and increasing the convergence speed. In the end, the test results on eight test functions show that the proposed algorithm improves the accuracy by 25% to 300% than basic algorithm in terms of optimal value and 50% to 100% for standard deviation.%针对入侵杂草优化算法存在的早熟现象,提出一种基于混沌序列的多种群入侵杂草优化算法.首先,算法初始化时,利用混沌序列初始化种群提高初始解的质量；其次,在算法迭代过程中,若个体的聚集程度小于阈值时,再次用混沌序列重新初始化种群,使得算法迭代过程中能够有效地跳出局部极小；最后,将杂草种群分为5个种群协同合作,可有效地避免算法早熟现象,提高算法的寻优精度和收敛速度.通过对8个测试函数的测试,结果表明,所提算法获得最优值比基本入侵杂草优化算法精度提高了25％ ～ 300％;标准差提高了50％～ 100％.
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