An Improved Ant Colony Optimization Algorithm for Improving Cloud Resource Utilization

2016 ◽  
Author(s):  
Qingbin Nie ◽  
Pinghua Li
Keyword(s):  
Ant Colony Optimization ◽  
Resource Utilization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Cloud Resource

scholarly journals AN Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud

2019 ◽  
Vol 8 (4) ◽  
pp. 421-429
Keyword(s):  
Ant Colony Optimization ◽  
Resource Utilization ◽  
Optimization Algorithm ◽  
Spanning Tree ◽  
Resource Provisioning ◽  
Ant Colony ◽  
Search Process ◽  
Ant Colony Optimization Algorithm ◽  
Hybrid Scheme ◽  
Resources Management

Paper Several Ant Colony Optimization (ACO) techniques for Cloud resources management are considered by many researchers. ACO techniques in existence still need some improvements for effective resource management and planning with the heterogeneous and voluminous services offered. Hence, an optimized hybrid scheme that combined deterministic characteristics for exploiting ACO search process is proposed. Spanning Tree (ST) algorithm was chosen in the hybridization that obtained a faster convergence speed, minimized makespan time and throughput that ensured resource utilization in least time and cost. Extensive experiments were conducted in cloudsim simulator provided an efficient result compared to other ACO techniques as it significantly improves performance.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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