Bat algorithm (BA) has the advantage of fast convergence, but there is still room for improvement in accuracy and stability of solution. An efficient and robust fusion bat algorithm (ERFBA) is proposed to overcome these defects. In the population reconstruction, an effective diversity population (EDP) is reconstructed by designing a multi-strategy opposition-based learning with disturbance. In the exploration, an adaptive constraint step whale optimization algorithm is presented to obtain the promising regions with fewer blind spots by exploring EDP. In the exploitation, we design a new BA local search strategy by novel combination between dynamic regulation and Cauchy mutation to get accurate and stable solution. Numerous experiments show that ERFBA has remarkable advantages in accuracy and stability for many high dimension, unimodal and multimodal problems. Moreover, the proposed algorithm is further tested and applied in areas of intelligent data analysis and intelligent design. The results show that the overall performance of the proposed ERFBA is better than other existing algorithms.

中文翻译：

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一种融合基于对立学习和鲸鱼优化算法的高效鲁棒蝙蝠算法

Bat算法（BA）具有收敛速度快的优点，但在精度和稳定性方面仍有改进的空间。为了克服这些缺陷，提出了一种高效，鲁棒的融合蝙蝠算法（ERFBA）。在人口重建中，通过设计具有干扰的基于多策略对立的学习来重建有效多样性人口（EDP）。在探索中，提出了一种自适应约束步鲸优化算法，通过探索EDP来获得盲点较少的有希望的区域。在开发中，我们通过动态调节和柯西突变之间的新颖结合，设计了一种新的BA局部搜索策略，以获得准确，稳定的解决方案。许多实验表明，ERFBA在许多高尺寸测量中，在精度和稳定性方面均具有显着优势，单峰和多峰问题。此外，该算法在智能数据分析和智能设计领域得到了进一步的测试和应用。结果表明，提出的ERFBA的总体性能优于其他现有算法。