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

作　　者：Tareq Hamadneh Khalid Kaabneh Omar Alssayed Kei Eguchi Zeinab Monrazeri Mohammad Dehghani 

机构地区：[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

出　　处：《Computer Modeling in Engineering & Sciences》2024年第11期1725-1808,共84页工程与科学中的计算机建模（英文）

摘　　要：In this article,a novel metaheuristic technique named Far and Near Optimization(FNO)is introduced,offeringversatile applications across various scientific domains for optimization tasks.The core concept behind FNO lies inintegrating global and local search methodologies to update the algorithm population within the problem-solvingspace based on moving each member to the farthest and nearest member to itself.The paper delineates the theoryof FNO,presenting a mathematical model in two phases:(i)exploration based on the simulation of the movementof a population member towards the farthest member from itself and(ii)exploitation based on simulating themovement of a population member towards the nearest member from itself.FNO’s efficacy in tackling optimizationchallenges 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 mosteffective optimizer across a majority of benchmark functions.Moreover,the outcomes derived by employing FNOon twenty-two constrained optimization challenges from the CEC 2011 test suite,alongside four engineering designdilemmas,showcase the effectiveness of the suggested method in tackling real-world scenarios.

关 键 词：OPTIMIZATION stochastic method FAR NEAR metaheuristic algorithm exploration EXPLOITATION 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]