郑州大学学报（工学版）2025，Vol.46Issue(6)：32-39,8.DOI:10.13705/j.issn.1671-6833.2025.03.017
基于适应度地形分析的优化算法调度方法
Optimization Algorithm Scheduling Method Based on Fitness Landscape Analysis
摘要
Abstract
Optimization algorithms often perform optimally on specific types of fitness terrains due to the varying na-ture of optimization problems.To address this limitation,in this study an optimization algorithm scheduling method grounded in fitness terrain analysis was introduced.This method characterizes the terrain features of an optimization problem by extracting the local peak cluster number features of the optimization objective function.Based on these terrain features,the method selected the most suitable algorithm to maximize the advantages of different algorithms through effective scheduling.In particular,this study considered the balance between exploration and exploitation in optimization problems by selecting the harris hawks optimization algorithm(HHO),known for its high develop-ment capability,and the differential evolution algorithm(DE),recognized for its strong exploration ability,as the scheduling algorithms.The choice of algorithm was tailored to the specific adaptability characteristics of the terrain.Experimental results show that the convergence performance of FL-AMAS was improved by 75%compared with that of HHO alone,and by 40%compared with that of DE algorithm.Further,FL-AMAS was compared with six ad-vanced algorithms,and FL-AMAS outperformed these algorithms in convergence accuracy on 75%of the benchmark set.The effectiveness and scalability of the proposed scheduling method were further validated through comparisons with other types of scheduling optimization algorithms.关键词
优化算法调度/适应度地形/特征提取/局部峰值点/哈里斯鹰优化算法/差分进化算法Key words
optimization algorithm scheduling/fitness landscape analysis/feature extraction/local peak points/harris hawks optimization algorithm/differential evolution algorithm分类
信息技术与安全科学引用本文复制引用
朱晓东,任春晓,刘晓兰,陈科,余春明..基于适应度地形分析的优化算法调度方法[J].郑州大学学报（工学版）,2025,46(6):32-39,8.基金项目
国家自然科学基金青年项目(62206255) （62206255）
中国博士后科学基金面上项目(2022M712878) （2022M712878）
