Open Access

ARTICLE

IRKO: An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

by R. Manjula Devi¹, M. Premkumar², Pradeep Jangir³, Mohamed Abdelghany Elkotb^4,5, Rajvikram Madurai Elavarasan⁶, Kottakkaran Sooppy Nisar^7,*

1 Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, 638060, Tamil Nadu, India
2 Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, 560078, Karnataka, India
3 Rajasthan Rajya Vidyut Prasaran Nigam, Sikar, 332025, Rajasthan, India
4 Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
5 Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Egypt
6 Clean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, 77553, TX, USA
7 College of Arts and Sciences, Prince Sattam bin Abdulaziz University, Wadi Aldawaser, 11991, Saudi Arabia

* Corresponding Author: Kottakkaran Sooppy Nisar. Email:

Computers, Materials & Continua 2022, 70(3), 4803-4827. https://doi.org/10.32604/cmc.2022.020847

Received 11 June 2021; Accepted 22 July 2021; Issue published 11 October 2021

Abstract

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version. The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems. The outcomes of IRKO are compared with seven state-of-the-art algorithms, including the basic RKO algorithm. Compared to other algorithms, the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems. The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems, including real-world optimization problems.

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Citations