City Research Online

Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm

Azizi, M., Talatahari, S. & Giaralis, A. ORCID: 0000-0002-2952-1171 (2021). Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm. IEEE Access, 9, pp. 102497-102519. doi: 10.1109/access.2021.3096726

Abstract

In this paper, optimum design of engineering problems is considered by means of the Atomic Orbital Search (AOS), a recently proposed metaheuristic optimization algorithm. The mathematical development of the algorithm is based on principles of quantum mechanics focusing on the act of electrons around the nucleus of an atom. For numerical investigation, 20 of well-known constrained design problems in different engineering fields are considered; some of which have been benchmarked by the 2020 Competitions on Evolutionary Computation (CEC 2020) for real-world optimization purposes. Statistical results including the best, mean, worst and standard deviation of multiple optimization runs are reported for the AOS algorithm. These results are compared to similar data from previous metaheuristic algorithms found in the literature to establish the efficiency and usefulness of the AOS. It is concluded that the AOS has acceptable behavior in dealing with all the considered constrained optimization problems while the maximum difference of about 40% between the best optimum values of the AOS and other approaches is noted for the robot gripper benchmark problem.

Publication Type: Article
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Atomic Orbital Search; Engineering Design; Competition on Evolutionary Computation; Constrained Optimization
Subjects: Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Departments: School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of Azizi_Talatahari_Giaralis_IEEE_Access_2021.pdf]
Preview
Text - Published Version
Available under License Creative Commons: Attribution International Public License 4.0.

Download (5MB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login