A Representation for Genetic-Algorithm-Based Multiprocessor Task Scheduling

Jelodar, Mehdi Salmani; Fakhraie, S.N.; Montazeri, Farhad; Fakhraie, S.M.; Ahmadabadi, Majid Nili

doi:10.1109/cec.2006.1688328

Cited by 10 publications

(3 citation statements)

References 20 publications

(35 reference statements)

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“…As observed, most of the works (29 in total) pay attention to the homogeneous environment, that is, when all the processors have the same processing capacity. Besides, 24 papers are related to the heterogeneous environment, and four works [27,28,43,49] deal with both scenarios. Azghadi et al [53] did not provide sufficient information about where their algorithm is applied; we believe, based on information presented in the publication, that they also considered both.…”

Section: Environments and Communication Costmentioning

confidence: 99%

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

Silva

Gabriel

2020

Computation

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The multiprocessor task scheduling problem has received considerable attention over the last three decades. In this context, a wide range of studies focuses on the design of evolutionary algorithms. These papers deal with many topics, such as task characteristics, environmental heterogeneity, and optimization criteria. To classify the academic production in this research field, we present here a systematic literature review for the directed acyclic graph (DAG) scheduling, that is, when tasks are modeled through a directed acyclic graph. Based on the survey of 56 works, we provide a panorama about the last 30 years of research in this field. From the analyzes of the selected studies, we found a diversity of application domains and mapped their main contributions.

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Section: Environments and Communication Costmentioning

confidence: 99%

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

Silva

Gabriel

2020

Computation

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“…In [16], a novel representation called fixed task, alternative processors (FTAP) was proposed to schedule the tasks. In this work, the length of chromosomes is intelligently adapted according to the given problem.…”

Section: Related Workmentioning

confidence: 99%

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

Bonyadi

Moghaddam

2009

Int J Parallel Prog

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Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a geneticbased algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.

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“…In literature set of related tasks is presented as a Directed Acyclic Graph (DAG scheduling problem). This issue was studied in [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Problem-Independent Approach to Multiprocessor Dependent Task Scheduling

Krol¹,

Zydek²,

Koszalka³

2012

International Journal of Electronics and Telecommunications

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This paper concerns Directed Acyclic Graph task scheduling on parallel executors. The problem is solved using two new implementations of Tabu Search and genetic algorithm presented in the paper. A new approach to solution coding is also introduced and implemented in both metaheuristics algorithms. Results given by the algorithms are compared to those generated by greedy LPT and SS-FF algorithms; and HAR algorithm. The analysis of the obtained results of multistage simulation experiments confirms the conclusion that the proposed and implemented algorithms are characterized by very good performance and characteristics.

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A Representation for Genetic-Algorithm-Based Multiprocessor Task Scheduling

Cited by 10 publications

References 20 publications

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

Problem-Independent Approach to Multiprocessor Dependent Task Scheduling

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