An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm
作者机构：Software CollegeNortheastern UniversityShenyang110169China School of Information Science and EngineeringShenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co.Ltd.Beijing100035China College of Computer Science and EngineeringNingxia Institute of Science and TechnologyShizuishan753000China
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第79卷第6期
页 面：5201-5223页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：support from the Ningxia Natural Science Foundation Project(2023AAC03361)
主 题：Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms
摘 要：The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.
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