Some metaheuristic algorithms for global optimization

Abstract

Optimization is a branch of applied mathematics and engineering that investigates finding the best solution from a set of possible solutions to achieve a particular goal, such as minimizing cost, maximizing efficiency, or maximizing profitability. Optimization issues are common in multiple fields such as engineering, economics, logistics, and artificial intelligence. Metaheuristics are general algorithms used to solve complex optimization problems that may be difficult or impossible to solve using traditional methods. Metaheuristics are characterized by their ability to handle a wide range of issues and to optimize in a large and diverse solution space. To evaluate performance, a variety of industry-standard and well-known issues are used. These issues are used as benchmarks to evaluate the algorithm's ability to find the optimal solution, the speed of reaching solutions, and the stability of the performance. In this thesis, we have solved geometric problems using the well-known metaheuristic methods.