一种新型的区间-粒子群优化算法

提出一种区间算法与粒子群算法相结合的新型优化算法.该算法改善了传统区间算法中存在的效率低及构造加速工具困难的问题,使区间算法可以更好地运用于高维模型.利用区间思想为新粒子的产生提供指导,并且利用粒子群算法的大范围随机搜索能力不断改进区间中心点的位置.随着算法迭代代数的增加,变量区间不断缩减,最终实现寻找全局最优目标区间的目的.对一些高维多峰值全局优化问题进行了仿真实验,结果表明该算法比传统区间优化算法更加有效.

New Interval-Particle Swarm Optimization Algorithm

A new optimized algorithm was proposed based on the interval-particle swarm combination algorithm, which improved the low efficiency and difficulty in constructing acceleration tools of traditional interval algorithm and made the interval algorithm better to be used in high-dimensional model. The interval theory was used in the improved algorithm to guide the production of new particles, and the random search capability of particle swarm optimization was used to improve interval center position. With the increase of the iterative step, the variable intervals were continuously reduced and the optimal interval could be eventually obtained. Simulations were carried out for the high-dimensional and multi-peak global optimization. The results showed that the improved algorithm was more efficient than the traditional interval algorithm.