A Hybrid Multiprocessor Task Scheduling Method Based on Immune Genetic Algorithm

Azghadi, Mostafa Rahimi; Bonyadi, Mohammad Reza; Hashemi, Seyed Hossein; Moghadam, Mohsen Ebrahimi

doi:10.4108/icst.qshine2008.4263

Cited by 5 publications

(8 citation statements)

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“…Thewell known heuristics such as 'Earliest Deadline First (EDF)' and 'Shortest Computation Time First (SCTF)' has been hybrid with the Genetic Algorithm (GA). It was concluded that hybridization of GA with SCTF provides better performance as compared to the EDF based hybrid GA.Azghadi et al [16] developed an immune genetic approach for multiprocessor task scheduling problem and proves to be more effective.Jouglet et al [17] proposed a memetic algorithm (MA) for the hybrid flow shop scheduling with multiprocessor tasks. They hybridized theGenetic Algorithm (GA) and Constraint Programming (CP) for a Memetic Algorithm (MA) and concluded the superiority of proposed MA.Hwang et al [18] proposed priority-based GA which uses a new encoding mechanism with a multi-functional chromosome that uses the priority representation-so-called priority-based multi-chromosome (PMC).…”

Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

Dhingra¹,

Dhingra²,

Gupta³

et al. 2016

ACII

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Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most Keywords:Multiprocessor task scheduling, heuristics, Hybrid Genetic algorithm, makespan, Total Completion time. 1.INTRODUCTIONParallel processing is the most promising approach for meeting the increased computational requirements thathas introduced a number of problems including multiprocessor task scheduling. Usually a large program is divided into smaller tasks havingsome dependencies representing the precedence constraints such that a task cannot be started until all its predecessors have finished. The goal of a task scheduling algorithm is to schedule all the tasks on the given number of available processors without violating precedence constraints so as to minimize the different performance measures in order to maximize the throughput and utilization of the system. The multiprocessor task scheduling problem with precedence constraints belongs to the class of NP hard problems and its importance led to several comparative studies. The conventional research on scheduling considers heuristic based algorithms which are divided into three categories: list scheduling, clustering based and duplication based. Among heuristic algorithms, List scheduling algorithms are commonly used and work by assigning priority to each task according to attributes such as t-level (top level), b-level (bottom level), static level (sl) and ALAP (As Late As Possible)

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Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

Dhingra¹,

Dhingra²,

Gupta³

et al. 2016

ACII

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show abstract

“…There are several approaches to solve multiprocessor task scheduling, such as heuristic approaches [13,18,47], evolutionary or population-based approaches [12,14,15,21,34,42,43] and hybrid methods [3]. In this section, several presented methods in the field of multiprocessor task scheduling from 1995 are reviewed.…”

Section: Related Workmentioning

confidence: 99%

“…The results showed that the combinatorial could work efficiently and scheduled the tasks on several processors as well as the other methods. The methods presented in [1,3], also, tried to combine the heuristic and genetic approaches to solve this problem.…”

Section: Related Workmentioning

confidence: 99%

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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 addition, the simplicity of the CAPET enabled us to use simple GA operators which influence the execution time of the algorithm. Moreover, to overcome the regeneration phenomena that is usually occurred in GA, an artificial immune system operator is employed to help exploring better results from the search space [9,10]. The proposed method was applied on several benchmarks and results were compared to several state-of-the-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

Moghaddam

Bonyadi

2011

Int J Parallel Prog

Self Cite

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Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the solutions (90% improvement in average) when it is applied to standard test benches.

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A Hybrid Multiprocessor Task Scheduling Method Based on Immune Genetic Algorithm

Cited by 5 publications

References 0 publications

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

Hybrid Genetic Algorithm for Bi-Criteria Multiprocessor Task Scheduling with Communication Delay

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

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