Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.

中文翻译：

---

HSCWMA：一种用于解决优化问题的新混合 SCA-WMA 算法

混合元启发式算法最近已成为解决优化问题的一个有趣话题。本文将啄木鸟交配算法（WMA）和正余弦算法（SCA）相结合，提出了一种混合元启发式算法来解决优化问题，称为HSCWMA。尽管 WMA 算法具有很高的探索能力，但该算法需要增强利用能力，尤其是在初始迭代中。此外，SCA 中使用的正弦和余弦关系为该算法提供了良好的利用，但 SCA 缺乏执行有效探索的有效过程。在HSCWMA中，利用Levy飞行机制修正的SCA数学搜索函数来更新WMA中的雌性啄木鸟。而且，本地搜索内存用于所提出的混合算法中的所有搜索元素。提出 HSCWMA 的目的是利用 WMA 和 Levy 飞行的勘探能力，利用 SCA 的开发敏感性和本地搜索记忆，开发勘探和开发资格，并提供这两个阶段之间的动态平衡。对于效率评估，所提出的算法在 28 个数学基准函数上进行了测试。HSCWMA 算法已与一系列最新和流行的元启发式算法进行了比较，它在解决非凸、不可分和高度复杂的优化问题方面优于它们。所提出的算法还用作多层感知器 (MLP) 神经网络训练器，以解决三个真实世界数据集上的软件开发工作量估计 (SDEE) 问题。仿真结果证明了 HSCWMA 算法在大多数评估中的优越和有希望的性能。