带认知因子的交叉鸽群算法

A Crossed Pigeon-inspired Optimization Algorithm with Congnitive Factor

摘要:

鸽群优化算法在求解最优问题时易早熟收敛，陷入局部最优，因此本文提出了带认知因子的交叉鸽群算法。首先，将地图指南针算子和地标算子进行联合交叉运行；然后，在地图和指南针算子中引入了非线性递增的认知因子，并将其视为运动权值的三角函数；最后，在地标算子中，引入呈三角函数递增的压缩因子，增加算法的平滑性。仿真结果表明，改进后的算法搜索成功率有很大的提高，能有效地避免早熟收敛，跳出局部极值，具有更好地寻优能力。

Abstract:

In solving optimal problems, pigeon-inspired optimization algorithm (PIO) is easy to premature convergence and trap in local optimum, so this paper presents a cross pigeon-inspired optimization algorithm with cognitive factors. Firstly, map the compass operator and landmark operator no longer run independently, and them are mixed together and operated crosswise; Second, in the map and compass operator the cognitive factor of nonlinear increment was introduced, and regard as the inertia weight’s trigonometric functions; Finally, in the landmark operator, a compressive factor that was increasing gradually in the form of trigonometric functions was proposed to make path smoother. Simulation results showed that the improved algorithm search success rate had greatly improved, and not only effectively avoid premature convergence, but also jump out of local minima and had better optimization ability.