渐进式分组狩猎的灰狼优化算法及其工程应用

针对灰狼优化（GWO）算法在求解复杂优化问题时存在后期收敛速度慢、易陷入局部最优的不足，提出了一种渐进式分组狩猎的灰狼优化（PGGWO）算法。首先，设计了非线性多收敛因子以增强全局勘探能力、避免局部最优；其次，提出了渐进式位置更新策略，该策略引入长鼻浣熊的包围策略和动态权重因子，前者在提高收敛精度和速度的同时避免局部最优，后者则动态地提升算法的收敛速度及全局寻优性能。最后，通过与标准GWO、4个GWO先进变体以及4个竞争力较强的新型进化算法对比，验证了PGGWO算法的有效性和先进性。在24个Benchmark函数和3个实际工程优化问题上的实验结果表明，PGGWO算法在收敛精度和收敛速度上具有明显优势，并且对约束优化问题也是有效的。

Grey wolf optimization algorithm based on progressive grouping hunting mechanism and its engineering applications

Focus on the shortcomings of the grey wolf optimization(GWO) algorithm in solving complex optimization problems, such as slow convergence speed and easy to fall into local optimum, this paper proposed a grey wolf optimization algorithm based on progressive grouping hunting mechanism(PGGWO).Firstly, nonlinear multi convergence factors were designed to enhance the global exploration ability and avoid local optimum. Secondly, a progressive location update strategy was proposed. The strategy introduced the encirclement strategy of coati and dynamic weight factors. The former avoided local optimum while improving convergence accuracy and speed, and the latter dynamically improved the convergence speed and global optimization performance of the algorithm. Finally, through comparing with GWO, 4 advanced GWO variants and 4 new with strong competitiveness, the experiment verifies the effectiveness and advancement of PGGWO. The experimental results on 24 Benchmark functions and 3 practical engineering optimization problems show that PGGWO algorithm has obvious advantages in convergence accuracy and convergence speed, and is also effective for constrained optimization problems.