The bat algorithm (BA) is one of prominent swarm-based algorithm that has the suitability in solving only small dimension engineering problems and suffers from drawback of getting trapped in local minimum with slow convergence for multi-dimensional problems. In the context of improving its applicability in solving large scale and constrained engineering design problems, this paper presents a novel upgraded bat algorithm with cuckoo search and Sugeno inertia weight (UBCSIW). In the proposed UBCSIW algorithm, first, the bat algorithm with its competence to exploit the optimal solutions in search space is combined with cuckoo search with its ability to explore best solution globally using Levy flight in the search space. Secondly, a new velocity and position search equation is incorporated in which the bat searches around the best candidate solution. This step helps in establishing adequate balance between exploration and exploitation capability and improving the performance effectively by employing greedy selection to choose the best candidate solution. Finally, Sugeno fuzzy inertia weight is introduced in the velocity updation equation, boosting the flexibility and diversity of bat population and results in stability of results. The effectiveness of the proposed UBCSIW algorithm is tested on 16 standard benchmark functions (unimodal and multimodal) with different dimensions, 12 CEC2015 test functions and 7 well-known constrained engineering design problems. The outputs of the proposed UBCSIW algorithm are validated by comparison with classical BA and other swarm-based state-of-the art algorithms. The simulation results show that proposed UBCSIW algorithm achieves highly competitive results in terms of higher optimization accuracy and improved convergence that outperforms basic BA in all twenty-eight test functions while performs better than other competitive algorithms in 24 functions (13 benchmark and 11 CEC2015 functions).

中文翻译：

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一种基于布谷鸟搜索和Sugeno惯性权重的新型蝙蝠升级算法求解大规模约束工程设计优化问题

蝙蝠算法（BA）是一种突出的基于群的算法，它适用于仅解决小维工程问题，但存在陷入局部最小值且多维问题收敛缓慢的缺点。在提高其在解决大规模和受约束的工程设计问题中的适用性的背景下，本文提出了一种新颖的升级蝙蝠算法，该算法具有布谷鸟搜索和 Sugeno 惯性权重（UBCSIW）。在所提出的 UBCSIW 算法中，首先，蝙蝠算法具有在搜索空间中开发最优解的能力，与布谷鸟搜索相结合，它能够在搜索空间中使用 Levy 飞行来全局探索最佳解。其次，结合了新的速度和位置搜索方程，其中蝙蝠搜索最佳候选解决方案。这一步有助于在探索和开发能力之间建立足够的平衡，并通过采用贪婪选择来选择最佳候选解决方案来有效提高性能。最后，在速度更新方程中引入了Sugeno模糊惯性权重，提高了蝙蝠种群的灵活性和多样性，并导致结果的稳定性。所提出的 UBCSIW 算法的有效性在 16 个不同维度的标准基准函数（单峰和多峰）、12 个 CEC2015 测试函数和 7 个著名的约束工程设计问题上进行了测试。所提出的 UBCSIW 算法的输出通过与经典 BA 和其他基于群的最先进算法进行比较来验证。