自适应的分数阶达尔文粒子群优化算法

doi:10.3969/j.issn.1000-436x.2014.04.015

摘要/Abstract

摘要：

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。

关键词: 分数阶达尔文粒子群优化, 进化因子, 分数阶次, 加速系数, ；变异机制, 自适应

Abstract:

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.

Key words: fractional-orderDarwinian particle swarm optimization, evolution factor, fractional-order, acceleration coef-ficients; mutation mechanism, adaptive

郭通,兰巨龙,李玉峰,陈世文. 自适应的分数阶达尔文粒子群优化算法[J]. 通信学报, 2014, 35(4): 130-140.

Tong GUO,Ju-long LAN,Yu-feng LI,Shi-wen CHEN. Adaptive fractional-order Darwinian particle swarm optimization algorithm[J]. Journal on Communications, 2014, 35(4): 130-140.

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状态	c₁	c₂
探测	增加	减少
开拓	微增	微减
收敛	微增	微增
跃出	减少	增加