Multi-population invasive weed optimization algorithm based on chaotic sequence

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.