Open Access

ARTICLE

Far and Near Optimization: A New Simple and Effective Metaphor-Less Optimization Algorithm for Solving Engineering Applications

Tareq Hamadneh^1,2, Khalid Kaabneh³, Omar Alssayed⁴, Kei Eguchi^5,*, Zeinab Monrazeri⁶, Mohammad Dehghani⁶

1 Department of Matematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Jadara Research Center, Jadara University, Irbid, 21110, Jordan
3 Faculty of Information Technology, Al-Ahliyya Amman University, Amman, 19328, Jordan
4 Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
5 Department of Information Electronics, Fukuoka Institute of Technology, Fukuoka, 811-0295, Japan
6 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran

* Corresponding Author: Kei Eguchi. Email:

(This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)

Computer Modeling in Engineering & Sciences 2024, 141(2), 1725-1808. https://doi.org/10.32604/cmes.2024.053236

Received 28 April 2024; Accepted 28 July 2024; Issue published 27 September 2024

Abstract

In this article, a novel metaheuristic technique named Far and Near Optimization (FNO) is introduced, offering versatile applications across various scientific domains for optimization tasks. The core concept behind FNO lies in integrating global and local search methodologies to update the algorithm population within the problem-solving space based on moving each member to the farthest and nearest member to itself. The paper delineates the theory of FNO, presenting a mathematical model in two phases: (i) exploration based on the simulation of the movement of a population member towards the farthest member from itself and (ii) exploitation based on simulating the movement of a population member towards the nearest member from itself. FNO’s efficacy in tackling optimization challenges is assessed through its handling of the CEC 2017 test suite across problem dimensions of 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results underscore FNO’s adeptness in exploration, exploitation, and maintaining a balance between them throughout the search process to yield viable solutions. Comparative analysis against twelve established metaheuristic algorithms reveals FNO’s superior performance. Simulation findings indicate FNO’s outperformance of competitor algorithms, securing the top rank as the most effective optimizer across a majority of benchmark functions. Moreover, the outcomes derived by employing FNO on twenty-two constrained optimization challenges from the CEC 2011 test suite, alongside four engineering design dilemmas, showcase the effectiveness of the suggested method in tackling real-world scenarios.

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Keywords

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