Wildebeest optimization algorithm based on swarm intelligence method in solving optimization problems

Metaheuristic algorithms are an effective way to solve optimization problems and use existing phe- nomena in nature to solve these problems. Due to the independence of metaheuristic algorithms from the gradient information, the objective function can be used to solve large-scale problems by optimization solutions. The organisms' behavior in nature in their interaction with each other is one of the optimization methods that are modeled as swarm-based algorithms. Swarm-based algorithms are a set of metaheuristic algorithms which are modeled based on group behavior of their organisms and social interactions. The behavior of wildebeests in nature is considered as a swarm-based algo- rithm for survival because it can be seen that these organisms migrate in groups and try to survive for themselves and their own herd. In this paper, a new metaheuristic algorithm (WOA) based on migratory and displacement behavior of wildebeests is presented of solving optimization problems. In this algorithm, problem solutions are denffed as wildebeest herds that search the problem space for appropriate habitat. The results of the implementation of a set of benchmark functions for solving optimization problems such as the Wildebeest Optimization Algorithm, Whale Optimization Algo- rithm, BAT, Fire y and Particle Swarm Optimization (PSO) algorithms show that the proposed algorithm is less error rate to nd global optimum and also caught up rate in the local optimum is less than the methods.