Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization

作     者：Saad-Eddine Chafi Younes Balboul Mohammed Fattah Said Mazer Moulhime El Bekkali 

作者机构：the Laboratory of Artificial IntelligenceData Sciences and Emerging SystemsNational School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco the LIA LaboratoryMoulay Ismail UniversityMeknes 50050Morocco 

出 版 物：《Intelligent and Converged Networks》 (智能与融合网络（英文）)

年 卷 期：2023年第4卷第4期

页      面：273-279页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：particle swarm optimization genetic algorithm performance evaluation edge and fog cloud FogWorkflowSim 

摘      要：Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization *** Algorithm(GA)is widely popular due to its logical approach,broad applicability,and ability to tackle complex issues encountered in engineering ***,GA is known for its high implementation cost and typically requires a large number of *** the other hand,Particle Swarm Optimization(PSO)is a relatively new heuristic technique inspired by the collective behaviors of real *** GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches *** they are often seen as competitors,their efficiency heavily relies on the parameter values chosen and the specific optimization problem at *** this study,we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud *** extensive experiments and performance evaluations,the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim *** comparative analysis sheds light on the strengths and limitations of each algorithm,providing valuable insights for researchers and practitioners in the field.