The principle and optimal solution logic of crow search algorithm

The Crow Search Algorithm (CSA) is a swarm intelligence optimization algorithm inspired by crow behavior. Similar to other nature-inspired metaheuristics, CSA simulates the behavior of crows hiding and retrieving food. This algorithm has the characteristics of simple structure, few control parameters, and easy implementation. However, like most optimization algorithms, CSA also has some shortcomings, such as slow convergence and easy to fall into local optimal solutions. Despite this, CSA is still a promising algorithm that can show good optimization capabilities on certain problems.

Why use the crow search algorithm?

The Crow Search Algorithm (CSA) is characterized by its ability to collect responses from different things and calculate them as a whole to find the best solution to the problem. The advantage of this approach is the ability to find the best solution for a specific problem. Therefore, the reason for adopting CSA is that it can provide effective problem solving.

Simply put, the concept of decentralized, self-organized collective behavior is used to solve specific problems.

Crow Behavior Logic

Crows are considered the most intelligent birds, and they have demonstrated self-awareness and the ability to make tools. In mirror tests, crows can recognize each other's faces and warn each other of approaching rivals. In addition, crows can use tools to engage in complex communication and recall the location of meals months later.

Crow Search Algorithm (CSA) Logic

The purpose of this heuristic is to have a crow find a hidden food location based on another crow. The crow's position is constantly updated throughout the process. Additionally, crows need to change their position when food is stolen.

The algorithm setting has a d-dimensional environment in which there are many crows. Vectors specifying the number of crows (group size) and their positions at each iteration. Every crow has a memory that holds its hiding place. On each iteration, the location of the crow's hiding place is revealed.

In the next iteration, the crow intends to travel to its hiding location, which is designated by another crow. In this iteration, the first crow chooses to follow the second crow to its hiding place. In this case, there are two possible outcomes.

The second crow didn’t know that the first crow had been behind it. As a result, the first crow will move closer to where the second crow is hiding. In this case, the new position of the first crow is obtained with the help of random numbers uniformly distributed between 0 and 1 and the flight length at the time of iteration.

The second crow knows that the first crow is following it, and in order to protect the collection from being stolen, it will deceive the first crow by changing its position in the search space.

In the Crow Search Algorithm (CSA), the perceptual probability parameter is mainly responsible for reinforcement and diversification. The Crow algorithm prefers to search the local area where the current optimal answer is by reducing the perceived probability value.

When the perceived probability value increases, the possibility of the algorithm searching in an area with existing results will decrease, and the Crow Search Algorithm (CSA) will be more inclined to randomly explore the search space. Therefore, using high awareness probability parameter values ​​can enhance the diversity of algorithm results.

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