A Quasi-Parallel Realization of the Investment Frontier in Computer Resource Allocation Using Simple Genetic Algorithm on a Single Computer

2002 ◽  
pp. 116-126 ◽  
Author(s):  
Kwok Yip Szeto ◽  
Rui Jiang
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Computer Resource ◽  
Simple Genetic Algorithm

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

2017 ◽  
Vol 26 (1) ◽  
pp. 169-184 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Nneoma A. Okoroafor ◽  
Seyed M. Buhari ◽  
Marc E. Frincu ◽  
Sahalu B. Junaidu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Grid Computing ◽  
Resource Selection ◽  
Computing System ◽  
Population Based ◽  
Fine Tuning ◽  
Grid Resource ◽  
Performance Results ◽  
Grid Resources

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

scholarly journals Dynamic Beam Hopping Time Slots Allocation Based on Genetic Algorithm of Satellite Communication under Time-Varying Rain Attenuation

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2909
Author(s):  
Chen Zhang ◽  
Jiangtao Yang ◽  
Yong Zhang ◽  
Ziwei Liu ◽  
Gengxin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Satellite Communication ◽  
Rain Attenuation ◽  
Time Varying ◽  
Dynamic Allocation ◽  
Satellite Systems ◽  
Flexible Resource ◽  
Conventional Methods ◽  
High Level

Beam hopping technology is considered to provide a high level of flexible resource allocation to manage uneven traffic requests in multi-beam high throughput satellite systems. Conventional beam hopping resource allocation methods assume constant rainfall attenuation. Different from conventional methods, by employing genetic algorithm this paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. Firstly, a beam hopping system model as well as rain attenuation time series based on Dirac lognormal distribution are provided. On this basis, the dynamic allocation method by employing genetic algorithm is proposed to obtain both quantity and arrangement of time slots allocated for each beam. Simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the non-uniform traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.

scholarly journals A Multiple Mitosis Genetic Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 252
Author(s):  
K. Kamil ◽  
K.H Chong ◽  
H. Hashim ◽  
S.A. Shaaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Total Population ◽  
Global Optimum ◽  
Natural Process ◽  
Local Optimum ◽  
Simple Genetic Algorithm ◽  
Cell Reproduction ◽  
Solve Optimization Problem ◽  
High Diversity

<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

scholarly journals Designing a competent simple genetic algorithm for search and optimization

2000 ◽  
Vol 36 (12) ◽  
pp. 3757-3761 ◽  
Author(s):  
Patrick Reed ◽  
Barbara Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Simple Genetic Algorithm
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