# An Advanced Crow Search Algorithm for Solving Global Optimization Problem
## 1 Introduction  
The optimization of engineering problems is of great interest to many researchers, and various strategies for incorporating optimization into the engineering field are being studied [1]. As an example, metaheuristic algorithms that are easy to apply to engineering problems are being developed for optimization. These algorithms are applied to various fields in order to optimize engineering problems by minimizing costs, shortening paths, and maximizing performance. Metaheuristic algorithms originated in 1965 with the development of the evolution strategy (ES) algorithm [2], and algorithms using various natural phenomena have been proposed. Figure 1 classifies the metaheuristic algorithms based on the natural phenomena that they emulate. Metaheuristic algorithms can be classified into four main categories: evolutionary, swarm, physicic, and human behavior [3; 4; 5; 6; 7]. Evolution-based algorithms are based on the genetic characteristics and evolutionary methods of nature, and representative algorithms include ES, evolutionary programming (EP), genetic algorithm (GA), genetic programming (GP), and differential evolution (DE). Swarm-based algorithms are based on the behavior of organisms such as birds or ants in clusters, and representative algorithms include ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), cuckoo search, and crow search (CS). Physical-based algorithms are based on physical phenomena, and representative algorithms include simulated annealing (SA), harmony search (HS), gravitational search (GS), black hole (BH), and sine cosine (SC). Finally, the human behavior-based algorithms are based on human intelligent behavior, and representative algorithms include human-inspired (HI), social emotional optimization (SEO), brain storm optimization (BSO), teaching learning-based optimization (TLBO), and social-based (SB) [8]. All metaheuristic algorithms perform optimization using exploitation and exploration. If the metaheuristic algorithm mainly uses exploration, then it can easily find the global minima but has a difficult time finding the exact solution. Conversely, metaheuristic algorithms which mainly use exploitation can find accurate solutions but are prone to falling into local minima [9; 10; 11]. Therefore, the convergence performance of the algorithm varies greatly depending on the method of using exploitation and exploration, and exploitation and exploration should be used in harmony [12]. Swarm-based algorithms are efficient in searching for global optima and are easy to apply to a variety of optimization problems. They also lend themselves well to parallelization, making them a popular choice for many researchers [13]. With these advantages, swarm-based algorithms are applied to various engineering fields, and many researchers are working to improve the convergence performance of algorithms. The conventional CS algorithm, originally proposed by [PERSON] in 2016 and ranked among the swarm-based algorithms, performs optimization by mimicking the high intelligence of crows [14]. Crow brains are intelligent enough to recognize food or humans because they are large compared to their body size. As crows are intelligent, they can remember the location of food hidden by other birds and steal this hidden food when the other birds are not around. The conventional CS algorithm proposes to perform optimization using these characteristics of crows and has the following four principles:  
* Crows live in groups. * Crows remember the location of their hidden prey. * Crows steal food from other birds. * Crows are protected by probability. The conventional CS algorithm utilizes a small number of parameters and demonstrates excellent convergence performance. Due to its easy application in problems and excellent performance, it is widely applied in civil and architectural engineering, electrical engineering, mechanical engineering, and image processing [15].