Introduction of Peripheral-Perpendicular Optimization with application in structural engineering

Roudak, Mohammad Amin; Shayanfar, Mohsen Ali; Farahani, Melika; Karamloo, Mohammad; Yavarikhah, Mona; Ebrahimpour, Elnaz

doi:10.22059/ceij.2025.388866.2226

Introduction of Peripheral-Perpendicular Optimization with application in structural engineering

Articles in Press

Authors

Mohammad Amin Roudak ¹

Mohsen Ali Shayanfar ²

Melika Farahani ¹

Mohammad Karamloo ³

Mona Yavarikhah ¹

Elnaz Ebrahimpour ¹

¹ Department of Civil Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.

² School of Civil Engineering, Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Tehran, Iran.

³ Department of Civil Engineering, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran.

10.22059/ceij.2025.388866.2226

Abstract

In this paper, a swarm-based metaheuristic optimization algorithm is proposed. The optimization process of this algorithm is conducted by a specific number of defined agents. These agents move through the search space based on their distance from the best candidate and using the combination of tangential- and perpendicular-direction movements. It dynamically adapts the movements to improve the search for optimal results. The agents explore a circular region to uncover potentially better solutions. The radius of this circle decreases gradually to provide a proper balance between exploration and exploitation. In order to validate the performance and efficiency of the presented algorithm, several mathematical and constrained engineering problems are analyzed. The performance of the algorithm is compared against other optimization methods. Based on the examples, the proposed method shows strong exploration and exploitation ability, while many other methods lack at least one of them. Moreover, the proposed method does not have many parameters to be highly sensitive to them. On the other hand, in all mathematical, engineering, and structural examples, the proposed method could successfully handle the local optima due to the combination of peripheral and perpendicular movements. These features together make the proposed method an efficient choice for solving optimization problems.

Keywords

Metaheuristics

Swarm-based Optimization

Constrained Optimization

Perimeter-Perpendicular Optimization

Subjects