The enhanced genetic algorithms for the optimization design

Guo, Pengfei; Wang, Xuezhi; Han, Yu

doi:10.1109/bmei.2010.5639829

Cited by 183 publications

(86 citation statements)

References 4 publications

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“…In our survey paper, we have considered decision tree [6], neural network [7] and GA [8] for resource management.…”

Section: Issues In Applying ML To Grid Rmsmentioning

confidence: 99%

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A Survey on Application of Machine Learning to Resource Management in Grid Environment

Singh¹,

Sarkar²,

Roy³

et al. 2013

LNSE

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Abstract-Grid represents an environment over a distributed area, incorporating heterogeneous elements such as server nodes, storage devices, and network components in a scalable, wide-area spanning compute infrastructure. Since a Grid requires large scale resource sharing, efficient resource management system (RMS) is required to manage the Quality of Service (QoS). One of the chief tasks of an RMS for Grid is selecting an appropriate and most suitable resource provider for execution of a particular job submitted by a user. This is also known as resource brokering. This paper presents a survey of resource brokering paradigms based on Machine Learning (ML). Three major ML techniques applied in Grid resource management for this purpose have been discussed.

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“…In our survey paper, we have considered decision tree [6], neural network [7] and GA [8] for resource management.…”

Section: Issues In Applying ML To Grid Rmsmentioning

confidence: 99%

“…GAs [21], [8] are function optimization or search heuristics that try to imitate the natural evolution process using selection, mutation and crossover operators. In a GA, a population of strings called chromosomes encode candidate solutions called to an optimization problem.…”

Section: Ga Based Resource Managementmentioning

confidence: 99%

A Survey on Application of Machine Learning to Resource Management in Grid Environment

Singh¹,

Sarkar²,

Roy³

et al. 2013

LNSE

View full text Add to dashboard Cite

show abstract

“…Therefore, the fitness function can be defined using Eq. 13 which is used by the genetic algorithm (Guo et al, 2010) in finding an optimal or a near optimal solution in determining the size for a data chunk:…”

Section: Algorithm Designmentioning

confidence: 99%

Matching Lsi for Scalable Information Retrieval

Palsonkennedy¹,

Gopal²

2012

Journal of Computer Science

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Latent Semantic Indexing (LSI) is one of the well-liked techniques in the information retrieval fields. Different from the traditional information retrieval techniques, LSI is not based on the keyword matching simply. It uses statistics and algebraic computations. Based on Singular Value Decomposition (SVD), the higher dimensional matrix is converted to a lower dimensional approximate matrix, of which the noises could be filtered. And also the issues of synonymy and polysemy in the traditional techniques can be prevail over based on the investigations of the terms related with the documents. However, it is notable that LSI suffers a scalability issue due to the computing complexity of SVD. This study presents a distributed LSI algorithm MR-LSI which can solve the scalability issue using Hadoop framework based on the distributed computing model Map Reduce. It also solves the overhead issue caused by the involved clustering algorithm by k-means algorithm. The evaluations indicate that MR-LSI can gain noteworthy improvement compared to the other scheme on processing large scale of documents. One significant advantage of Hadoop is that it supports various computing environments so that the issue of unbalanced load among nodes is highlighted.Hence, a load balancing algorithm based on genetic algorithm for balancing load in static environment is proposed. The results show that it can advance the performance of a cluster according to different levels.

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“…GA is stochastic search techniques based on the mechanism of natural selection and natural genetics. There are three major advantages to apply GA to optimization problems [22]. One is that GA does not have much mathematical requirements about the optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Genetic Algorithm and Fuzzy Decision Based Multi-objective Optimization in Microgrid with DGs

Xie¹,

Zhai²,

Liu³

et al. 2016

TOEEJ

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Abstract:Microgrid is one practical infrastructure to integrate Distributed Generations (DGs) and local loads. Its optimal operating strategy has aroused great attention in recent years. This paper mainly focuses on the multi-objective optimization of DGs in microgrid by using self-adaptive genetic algorithm (GA) and fuzzy decision. Five objective functions are taken into account comprising voltage offset, transmission loss, construction cost, purchase cost and the environmental cost. In the algorithm, selfadaptation in population size, mutation probability, selection and standardization of objective functions is developed to enhance the speed and efficiency of the algorithm. Moreover, fuzzy decision is applied to determine the final solution. Simulation results show this algorithm can effectively find the optimal solution and improve the real-time control of microgrid, which implies the possibility of potential applications in microgrid energy management system.

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The enhanced genetic algorithms for the optimization design

Cited by 183 publications

References 4 publications

A Survey on Application of Machine Learning to Resource Management in Grid Environment

A Survey on Application of Machine Learning to Resource Management in Grid Environment

Matching Lsi for Scalable Information Retrieval

Self-adaptive Genetic Algorithm and Fuzzy Decision Based Multi-objective Optimization in Microgrid with DGs

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