鹈鹕初始化的灰狼算法在工程优化上的应用

Application of Grey Wolf Algorithm Initialized by Pelican in Engineering Optimization

为了进一步提高灰狼算法(Grey Wolf Optimization, GWO)的开发能力和勘探能力,提出一种鹈鹕算法(Pelican Optimization Algorithm, POA)初始化灰狼位置的算法(POAGWO)。POAGWO算法利用POA算法来优化GWO算法的初始位置,这有助于增强POAGWO算法的开发能力和勘探能力。然后,通过不同类型的单峰,高维多模态和固定维多模态的6个典型的测试函数对算法的性能进行测试。测试结果表明POAGWO算法在开发能力和勘探能力上都超过了POA算法和GWO算法,并且从6个迭代曲线图中也可以看出改进后的算法可更快地趋向于全局最优解。最后将POAGWO算法应用于4个工程优化设计问题中,应用结果表明POAGWO算法具有很好的求解性能。

In order to further improve the development and exploration capabilities of Grey Wolf Optimization (GWO), a Pelican Optimization Algorithm (POA) is proposed to initialize the position of Grey Wolf (POAGWO). POAGWO algorithm uses POA algorithm to optimize the initial position of GWO algorithm, which helps to enhance the development ability and exploration ability of POAGWO al-gorithm. Then, six typical test functions of different types of single peak, high-dimensional multi-modal and fixed dimensional multimodal are used to test the performance of the algorithm. The test results show that POAGWO algorithm outperforms POA algorithm and GWO algorithm in both development and exploration capabilities, and it can also be seen from the six iteration curves that the improved algorithm can quickly approach the global optimal solution. Finally, POAGWO algo-rithm is applied to four engineering optimization design problems, and the application results show that POAGWO algorithm has good performance.