Sand Cat Optimization: Efficient Search Algorithm and Crowd Management

Meta-heuristic algorithms are optimization

methods inspired by natural behaviors and

processes that solve very complex optimization

problems by taking advantage of population

interaction and nature-inspired concepts. In this

study, a meta-heuristic algorithm called swarm

optimization of sand cats has been introduced,

which inspires the behavior of sand cats to sustain

life and maintain survival. These cats have a unique

and amazing ability to detect frequencies below 2

kHz and dig up soil to hunt for prey. Also, these

cats have a great ability to locate, move quickly,

and hide themselves from the sight of the prey.

Inspired by these features, the proposed algorithm

consists of two main phases of search and attack,

which manages the exploration and exploitation

phases in a balanced manner. This approach

provides optimal performance in solving various

congestion problems by reducing the number of

parameters and operations, providing useful and

efficient solutions to complex optimization

problems.