求解全局优化问题的改进灰狼算法

Modified grey wolf optimization algorithm for global optimization problems

灰狼算法是一种高效的优化技术,但其在一些问题上存在求解精度不高、收敛速度较慢和易于陷入局部最优的缺点。因此,提出了一种改进的灰狼优化算法(MGWO)。该算法引入了3种改进策略:平衡算法全局搜索性和局部开发性的指数规律收敛因子调整策略、提高算法求解精度的自适应位置更新策略和修订动态权重策略。通过两组在10个基准测试函数上的对比实验,验证3种改进策略的有效性。实验结果表明,综合使用3种策略的MGWO_4明显提升了基本灰狼算法(GWO)的性能,而且优于其他文献中的改进灰狼算法和其他数个优化算法。最后,在工程设计问题上的实验结果进一步证明了MGWO高效的寻优能力。

The grey wolf optimization algorithm is an efficient optimization technology,however,it still has some shortcomings,such as low accuracy,slow convergence speed and easy to fall into local optimum.Therefore,a modified grey wolf optimization algorithm(MGWO)was proposed.Three improved strategies were introduced into the algorithm:the strategy of the convergence factor adjustment with exponential law,the adaptive position updating strategy of grey wolf and the revised dynamic weight strategy.Through the implementation of two groups of comparative experiments on 10 benchmark functions,the effectiveness of the three improved strategies was verified.The experimental results show that the MGWO-4 with the three improved strategies improves the performance of the basic grey wolf optimization algorithm(GWO)significantly,and is superior to the improved grey wolf algorithm and several other optimization algorithms in other literatures.The experimental results on an engineering design question further prove that MGWO is a powerful optimization technique.