Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems.

Automated summary

This paper presents a novel metaheuristic optimization algorithm called the circle search algorithm (CSA) that is inspired by the geometrical features of circles. The CSA is evaluated against other algorithms through independent experiments using a variety of functions and engineering problems, and the results show that CSA outperforms other algorithms in terms of convergence speed and robustness to high-dimensional problems. Therefore, CSA is a promising algorithm for solving various optimization problems.