全局最优导向模糊布谷鸟搜索算法及应用

Global-best guided fuzzy cuckoo search algorithm and its application

针对标准布谷鸟搜索算法探索能力强而开发能力较弱、收敛速度慢及计算精度较差等问题,提出了具有全局最优导向的模糊布谷鸟搜索算法。在鸟窝更新公式中引入全局最优导向策略,在产生新的鸟窝位置时利用到当前最优鸟窝位置信息,以保持鸟窝的多样性并提高算法的开发能力。另外,采用模糊逻辑规则对布谷鸟算法中的搜索步长和外来鸟蛋被发现概率这2个重要参数进行自适应调整,以提高算法的全局收敛性能和求解精度。通过2个经典结构可靠性分析极限状态方程测试该算法的性能,并将其应用于某飞机舱门锁定机构可靠性分析中。实验结果表明,与粒子群算法、标准布谷鸟搜索算法和改进布谷鸟搜索算法相比,所提出的全局最优导向模糊布谷鸟搜索算法在进行可靠性分析中,能够有效地提高解的精度并增加收敛速度,寻优效果更优。

A global-best guided fuzzy cuckoo search algorithm is proposed to deal with the deficiencies of cuckoo search algorithm,such as poor at exploitation and accuracy,slow convergence,etc. A global-best guided strategy was introduced into the nests update formula to take advantage of the current optimal nest location information when producing new nest location in order to maintain the diversity of the nests and increase the algorithm＇s exploitation. In addition,the proposed method utilize fuzzy set theory to adjust the two main coefficients,one is search step,the other is the fraction of worst nests,and is thereby able to improve the accuracy and the global convergence. The performance of the proposed algorithm was tested by two classical structural reliability limited state functions and then it was applied to reliability analysis of an aircraft door locking mechanism. Experimental results show that compared with the particle swarm optimization,standard cuckoo search algorithm and improved cuckoo search algorithm,the proposed algorithm enhances the accuracy and the convergence effectively,and it has better optimization results when applied to reliability analysis problems.