An Advanced Crow Search Algorithm for Solving Global Optimization Problem

The conventional crow search (CS) algorithm is a swarm-based metaheuristic that has fewer parameters, easy to apply problems, and utilized in various fields. However, it disadvantage, as for fall into local minima by relying mainly on exploitation find approximations. Therefore, this paper, we propose the advanced (ACS) algorithm, which improves CS solves global optimization problem. ACS three differences from algorithm. First, using dynamic AP (awareness probability) perform exploration of region selection initial population. Second, improved performance introducing formula probabilistically selects best crows instead randomly selecting them. Third, phase adding an equation search. proposed paper over other algorithms both unimodal multimodal benchmark functions, found most optimal solutions five engineering problems.