Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.

中文翻译：

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具有平均最佳位置方向的高斯量子蝙蝠算法，用于数值函数优化。

具有平均最佳位置定向（QMBA）的量子行为bat算法是具有良好性能的bat算法（BA）的一种新颖变体。但是，QMBA算法生成的所有随机系数具有均匀的概率分布，只能提供相对较小的搜索范围，因此仍然面临一定程度的过早收敛。为了帮助蝙蝠逃脱局部最优，本文提出了一种新颖的具有均值最佳位置定向的高斯量子蝙蝠算法（GQMBA），该算法应用高斯概率分布生成随机数序列。在GQMBA中应用高斯分布而不是均匀分布来生成随机系数是提高性能的一种有效方法，可以避免过早收敛。在这篇文章中，将QMBA与高斯概率分布相结合，解决了数值函数优化问题。使用19个基准函数并将其与其他算法进行比较，以评估GQMBA的准确性和性能。实验结果表明，在大多数情况下，提出的GQMBA算法可以提供更好的搜索性能。