自适应的分数阶达尔文粒子群优化算法

针对分数阶达尔文粒子群算法收敛性能依赖于分数阶次α，易陷入局部最优的特点，提出了一种自适应的分数阶达尔文粒子群优化（AFO-DPSO）算法，利用粒子的位置和速度信息来动态调整分数阶次α，并引入自适应的加速系数控制策略和变异处理机制，以获取更优的收敛性能。对几种典型函数的测试结果表明，相比于现有的粒子群算法，所提的AFO-DPSO算法的搜索精度、收敛速度和稳定性都有了显著提高，全局寻优能力得到了进一步提高。

Adaptive fractional-order Darwinian particleswarm optimization algorithm

The convergence performance of the fractional-order Darwinian particle swarm optimization (FO-DPSO) al-gorithm depends on the fractional-order α, and it can easily get trapped in the local optima. To overcome such shortcom-ing, an adaptive fractional-order Darwinian particle swarm optimization (AFO-DPSO) algorithm was proposed. In AFO-DPSO, both particle's position and velocity information were utilized adequately, together an adaptive acceleration coefficient control strategy and mutation processing mechanism were introduced for better convergence performance. Testing results on several well-known functions demonstrate that AFO-DPSO substantially enhances the performance in terms of convergence speed, solution accuracy and algorithm stability. Compared with PSO, HPSO, DPSO, APSO, FO-PSO, FO-DPSO and NCPSO, the global optimality of AFO-DPSO are greatly improved.