ABSTRACT Sine-cosine algorithm (SCA) has found a widespread application in various engineering optimization problems. However, SCA suffers from premature convergence and insufficient exploitation. Cylindricity error evaluation is a typical engineering optimization problem related to the quality of cylindrical parts. A hybrid greedy sine-cosine algorithm with differential evolution (HGSCADE) is developed in this paper to solve optimization problems and evaluate cylindricity error. HGSCADE integrates the SCA with the opposition-based population initialization, the greedy search, the differential evolution (DE), the success history-based parameter adaptation, and the Levy flight-based local search. HGSCADE is tested on the CEC2014 benchmark functions and is employed in cylindricity error evaluation. The results show the superiority of HGSCADE to other state-of-the-art algorithms for the benchmark functions and cylindricity error evaluation.

中文翻译：

---

一种用于全局优化和圆柱度误差评估的差分进化混合贪心正弦余弦算法

摘要 正余弦算法（SCA）在各种工程优化问题中得到了广泛的应用。然而，SCA 存在早熟收敛和不充分利用的问题。圆柱度误差评估是一个典型的与圆柱零件质量相关的工程优化问题。本文开发了一种具有差分进化的混合贪婪正弦-余弦算法（HGSCADE）来解决优化问题并评估圆柱度误差。HGSCADE 将 SCA 与基于反对派的种群初始化、贪婪搜索、差分进化 (DE)、基于成功历史的参数自适应和基于 Levy 飞行的局部搜索相结合。HGSCADE 在 CEC2014 基准函数上进行测试，并用于圆柱度误差评估。