一种改进的变尺度混沌优化算法及其仿真研究

为了避免混沌优化在区间内的盲目重复搜索，提高搜索效率，提出一种改进的变尺度混沌优化方法，该方法在混沌搜索过程中，只需设置两个循环，内循环进行混沌搜索，外循环负责缩小区间．将每次搜索到的较优值计数，并设置一个标志A，当搜索到较优值的次数=A时，则根据搜索区间的大小动态缩小空间，在小区间中再重复上述过程，直至找到全局最优解，方法非常简单。仿真结果表明，该方法局部搜索能力强，搜索效果优于变尺度混沌优化方法。

Improving Mutative Scale Chaos Optimization Algorithm and Simulation Study

In order to avoid blind and repeated searching of chaos optimization in searching space and improve searching efficiency, an improving mutative scale chaos optimization algorithm was proposed. A couple of cycles are set, chaos search in the inner cycle and the range is reduced in the outer cycle. The algorithm counts better value for every searching and sets a sign A in the chaos searching, when the numbers of better value searched is equal to A, the searching space is dynamic reduced according scale, and the above course is repeated in the lesser scale till global optimal value is found. The simulation results show that algorithm is simple and local searching ability is better, the efficiency is higher than that of mutative scale chaos optimization.