A hybrid algorithm based on grey wolf optimization algorithm arithmetic optimization algorithm to solve optimization problem

The gray wolf optimization algorithm is a post-intuitive algorithm inspired by gray wolf behavior that was first proposed by Mirijalili in 2014. It is based on swarm intelligence and community intelligence. It outperforms other swarm intelligence systems in terms of simplicity, flexibility, ease of use, exploitability, and the unique ability to strike the perfect balance between exploration and exploitation during the research process. This work presents two gray wolf hybrid algorithms, one of which is based on two classical methods (Conjugate Gradient Algorithm) and the other on (Parallel Tangent Algorithm). The qualities of the aforementioned two classic methods are used to improve the basic community randomly formed as the gray wolf optimization’s main community. The statistical validity of most of these functions was determined by determining the minimum average values for many implementations. and the hybrid algorithms reached optimal solutions by attaining the most minimum value (f min). In addition, the algorithm’s outcomes are improved using arithmetic optimality (AOA). GWO-AOA, a new hybrid algorithm, finds the best numerical optimization solution, solves the global optimization problem, and avoids local solutions. GWO-AOA, a new hybrid algorithm, has been extensively tested on the optimization function and has outperformed the original algorithm. The suggested hybrid form may efficiently address benchmarks and real-world applications, with or without restricted and unknown search regions, according to numerical and statistical test results