In this study, a novel metaheuristic optimization algorithm, gradient-based optimizer (GBO) is proposed. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence rate to achieve better positions in the search space. The LEO enables the proposed GBO to escape from local optima. The performance of the new algorithm was evaluated in two phases. 28 mathematical test functions were first used to evaluate various characteristics of the GBO, and then six engineering problems were optimized by the GBO. In the first phase, the GBO was compared with five existing optimization algorithms, indicating that the GBO yielded very promising results due to its enhanced capabilities of exploration, exploitation, convergence, and effective avoidance of local optima. The second phase also demonstrated the superior performance of the GBO in solving complex real-world engineering problems. Source codes of the GBO algorithm are publicly available at http://imanahmadianfar.com/codes/.

在这项研究中，提出了一种新颖的元启发式优化算法，基于梯度的优化器（GBO）。受基于梯度的牛顿方法启发的GBO使用两个主要运算符：梯度搜索规则（GSR）和局部转义运算符（LEO）以及一组用于探索搜索空间的向量。GSR采用基于梯度的方法来增强探索趋势并加快收敛速度​​，从而在搜索空间中获得更好的位置。LEO使拟议的GBO摆脱了局部最优。新算法的性能分两个阶段进行评估。首先使用28个数学测试函数来评估GBO的各种特性，然后通过GBO优化了六个工程问题。在第一阶段，将GBO与五个现有的优化算法进行了比较，这表明，由于其增强的勘探，开发，融合和有效避免局部最优的能力，全球生物多样性组织取得了非常可喜的结果。第二阶段还展示了GBO在解决复杂的实际工程问题方面的卓越性能。GBO算法的源代码可从http://imanahmadianfar.com/codes/上公开获得。