采用Logistic混沌反向学习策略种群初始化，而改善蝗虫算法初始种群

Logistic chaotic reverse learning strategies employed population initialization, while improving the uniformity of hair and locust diversity algorithm initial population distribution is two steps, firstly the formula (. 7) Logistic chaotic mapping population initialization sequence. Logistic mapping is used sensitive to initial characteristics, the initial value given to the small differences in the micro-I of formula (7), i.e. to give chaotic variable y I

Using Logistic chaos reverse learning strategy population initialization, and improving the uniformity and diversity of the initial population distribution of locust algorithm is two steps, first of all, using the Logistic map to produce chaotic sequence of population initialization. Using the characteristics of Logistic mapping sensitivity to initial values, the initial value of the equation (7) is given to the small difference, i.e. i.e. I chaotic variable y

There are two steps to improve the uniformity and diversity of the initial population distribution of the locust algorithm. First, the chaotic sequence is generated by using (7) logistic map to initialize the population. Using the sensitivity of logistic map to the initial value, we give a small difference initial value to equation (7), i.e., I chaotic variable y<br>