柯西种群分布的自适应范围粒子群优化算法

为了提高粒子群优化算法的求解性能，提出了一种具有柯西种群分布的自适应范围搜索的粒子群优化算法(ARPSO/C)。该算法在种群服从柯西分布的假设下，在每一次迭代中利用个体分布的中位数和尺度参数来自适应地调整种群的搜索范围，从而在局部搜索和全局搜索之间达到了一个很好的平衡。最后的数值实验结果表明：与ARPSO和PSO算法相比，该算法收敛速度得到了显著提高，并且能够有效地克服早熟现象。

Adaptive range particle swarm optimization with the Cauchy distributed population

In order to improve the performance of the Particle Swarm Optimization (PSO), an adaptive range PSO with the Cauchy distributed population named ARPSO/C was proposed. The algorithm used the median and scale parameters to adjust self-adaptively the search range in population under the suppose of the individuals obeying the Cauchy distribution, thus balanced between local search and global search. The numerical comparison results on the proposed algorithm, ARPSO and PSO show that the presented algorithm has higher convergence speed and can overcome the prematurity.