【摘要】 针对标准粒子群算法寻优高维极值函数能力低,基本人工鱼群算法后期收敛速度慢,精度有待于提高等问题,提出了粒子群优化人工鱼群算法。上述算法综合利用了人工鱼群算法的良好全局收敛性、快速跳出局部极值的能力和粒子群算法信息策略、局部快速收敛性及简单操作易实现等优点。此外,引进了粒子的飞行速度和线性惯性权重特征,充分使用了两种算法的优点。通过仿真分析,验证了上述算法相比于两种基本算法具有更快的收敛速度和更高的寻优精度,且性能稳定。更多还原

【Abstract】 To slove the problem that the standard particle swarm optimition( PSO) algorithm has a low ability when applied to the optimization of multi-dimensional and multi-extreme value functions,and the convergence rate of artificial fish swarm( AFSA) algorithm can be slow and the precision is not high,an algorithm called PSO-AFSA was proposed in the paper. The algorithm synthesizes the global convergent performance and the quick jump out of the local minima of AFSA,the informational strategy and the quck local convergent performance of PSO which has the advantage of simply operate and easy to achieve. Moreover,this algorithm introduces the velocity and linear inertia weight characteristics of the particle,it makes full use of two’s advantages. Through simulation analysis,it is verified that the particle swarm optimization algorithm has faster convergence speed and higher precision than the two basic algorithms,and the performance is stable.更多还原