Solve global optimization problems based on metaheuristic algorithms

Abstract

Metaheuristic algorithms have evolved with exciting performance to solve complex real-world combinatorial optimization problems. These combinatorial optimization problems span across engineering, medical sciences, and sciences generally. In this paper we have proposed metaheuristic algorithms for solving the global optimization problems. The global optimization problems are one of interested problems in artificial intelligence, medical sciences, engineering and machine learning. We have discussed a number of algorithms such as Whale Optimization Algorithm (WOA), the Bat Algorithm (BA), War Strategy Optimization (WSO), and Ant Lion optimization algorithm (ALO). In our paper we have tested our algorithms on twenty-three benchmark functions. The numerical results show that the War Strategy optimization algorithm (WSO) has the best performance more than the other algorithms to solve global optimization problems, and the Bat Algorithm (BA) has the worst performance to solve the global optimization problems. The experimental results for various global optimization problems prove the superiority of the War strategy optimization algorithm.