Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows’ intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA), which has been tested and evaluated by a set of well-known benchmark functions. A new update mechanism that uses the merits of the global best position to move toward the best position is proposed. This mechanism increases the convergence of the algorithm and improves its local search-ability. Twenty benchmark functions are used to evaluate the performance of the proposed ICSA. Moreover, the ICSA algorithm is compared with the conventional CSA and other meta-heuristic algorithms such as particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), gray wolf optimizer (GWO), moth-flame optimization (MFO), and sine-cosine algorithm (SCA). The experimental result shows that the proposed ICSA algorithm has produced promising results and outperformed conventional CSA and other meta-heuristic algorithms. Also, the proposed ICSA has a more robust convergence for optimizing objective functions in terms of solution accuracy and efficiency.

元启发式算法在解决各种优化问题方面已显示出令人鼓舞的结果。乌鸦搜索算法（CSA）是一种新的有效的元启发式算法，可以模仿乌鸦在自然界中的智能群体行为。但是，它存在一些问题，例如陷入局部最优和过早收敛。本文提出了一种改进的乌鸦搜索算法（ICSA），该算法已通过一组著名的基准函数进行了测试和评估。提出了一种新的更新机制，该机制利用全局最佳位置的优点向最佳位置移动。这种机制增加了算法的收敛性，并提高了其局部搜索能力。二十个基准功能用于评估提议的ICSA的性能。而且，将ICSA算法与常规CSA和其他元启发式算法进行了比较，例如粒子群优化（PSO），蜻蜓算法（DA），蚱hopper优化算法（GOA），灰太狼优化器（GWO），飞蛾优化（MFO） ）和正弦余弦算法（SCA）。实验结果表明，所提出的ICSA算法取得了令人满意的结果，并且优于传统的CSA和其他元启发式算法。同样，拟议中的ICSA在解决方案准确性和效率方面具有更强大的收敛性，可用于优化目标函数。实验结果表明，所提出的ICSA算法取得了令人满意的结果，并且优于传统的CSA和其他元启发式算法。同样，拟议中的ICSA在解决方案准确性和效率方面具有更强大的收敛性，可用于优化目标函数。实验结果表明，所提出的ICSA算法取得了令人满意的结果，并且优于传统的CSA和其他元启发式算法。同样，拟议中的ICSA在解决方案准确性和效率方面具有更强大的收敛性，可用于优化目标函数。