A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow

Qian, Tao; Chang, Huiyou; Yi, Yang; Gu, Chenghong; Li, Wenjie

doi:10.1016/j.cor.2010.09.012

Cited by 38 publications

(14 citation statements)

References 36 publications

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“…Assume that the current individual S' is S'= (2,3,9,4,1,5,8,6,7) and the current city c is 3. If the generated random number rand() does not exceed p, another city c' from the same individual S' is selected (say, c' is 8), and appropriate segment is inverted, producing the following offspring S'= (2,3,8,5,1,4,9,6,7).…”

Section: An Improved Discrete Particle Swarm Optimization With Inmentioning

confidence: 99%

“…If the generated random number rand() does not exceed p, another city c' from the same individual S' is selected (say, c' is 8), and appropriate segment is inverted, producing the following offspring S'= (2,3,8,5,1,4,9,6,7). Otherwise (i.e., rand() > p), another individual is (randomly) selected from the population, assume, it is (1,6,4,3,5,7,9,2,8). This individual is searched for the city c' "next" to city 3 (which is 5), thus the segment for inversion in S' starts after city 3 and terminates after city 5.Consequently, the new offspring is S'=(2,3,5,1,4,9,8,6,7).…”

Section: An Improved Discrete Particle Swarm Optimization With Inmentioning

confidence: 99%

“…The project uses a huge pilot complex as the study background. Also many papers have been published to solve the scheduling problem [1,2,3], few papers have been published to discuss the ship pilot scheduling problem The remainder of this paper is organized as follows. The pilot model is discussed in Section II.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimum ship pilot plan model based on the modified discrete particle swarm optimization

Xue

Zhang

Fei

2014

11th IEEE International Conference on Control &Amp; Automation (ICCA)

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an optimum ship pilot plan model with unknown pilot number is presented. Based on the analysis of the difficulties to solve the problem, a pseudo travel salesman problem (TSP) model is presented to describe the model. A novel modified discrete particle swarm optimization algorithm base on the inver-over operator (IDPSO) is presented. Experiments carried out on the traveling salesman problem show that the modified IDPSO achieves good results comparing with the general DPSO. The new algorithm can improve both the convergence speed and solution precision. This modified algorithm is used to solve the optimum ship pilot plan problem. Simulations were carried and the results show that the pseudo travel salesman problem is very fit for describe the model. The computation with practical data shows that the model and the solving method are very effective.

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Section: An Improved Discrete Particle Swarm Optimization With Inmentioning

confidence: 99%

Section: An Improved Discrete Particle Swarm Optimization With Inmentioning

confidence: 99%

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Optimum ship pilot plan model based on the modified discrete particle swarm optimization

Xue

Zhang

Fei

2014

11th IEEE International Conference on Control &Amp; Automation (ICCA)

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“…The rotary chaotic particle swarm optimization (RCPSO) algorithm was proposed in [34] for solving the workflow scheduling problem in grid systems. This algorithm considers reliability as one of the quality of service (QOS) parameters, in addition to time and cost.…”

Section: Related Workmentioning

confidence: 99%

Using imperialist competition algorithm for independent task scheduling in grid computing

Pooranian

Shojafar

Javadi

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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A grid computing environment provides a type of distributed computation that is unique because it is not centrally managed and it has the capability to connect heterogeneous resources. A grid system provides location-independent access to the resources and services of geographically distributed machines. An essential ingredient for supporting location-independent computations is the ability to discover resources that have been requested by the users. Because the number of grid users can increase and the grid environment is continuously changing, a scheduler that can discover decentralized resources is needed. Grid resource scheduling is considered to be a complicated, NP-hard problem because of the distribution of resources, the changing conditions of resources, and the unreliability of infrastructure communication. Various artificial intelligence algorithms have been proposed for scheduling tasks in a computational grid. This paper uses the imperialist competition algorithm (ICA) to address the problem of independent task scheduling in a grid environment, with the aim of reducing the makespan. Experimental results compare ICA with other algorithms and illustrate that ICA finds a shorter makespan relative to the others. Moreover, it converges quickly, finding its optimum solution in less time than the other algorithms. . real implementations. The computations that are needed to simulate and study all aspects of scientific research projects require a significant amount of computational power that a single computer would take too much time to provide. Superscalar computers, vector processors, and pipeline processing were proposed to address this problem. Although they provide more computational power and greater speed than a single computer, technological limitations related to speed and the high cost of their design and manufacture make them available only to users with no financial limitations. Grid computations, which are based on distributed systems, were proposed to solve such problems. Grid systems have

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“…The drawback of this proposal is that the hillclimbing heuristic scans each particle which renders local search computation complex (Engelbrecht, 2005). Tao et al (2009) proposed RHDPSO and RCPSO (Tao et al, 2011b) to solve multi-QoS constrained grid workflow scheduling problem. PSO particles represent DAG task graphs; dimensions of a particle represent the tasks in the corresponding graph.…”

Section: Pso-based Grid Workflow Schedulingmentioning

confidence: 99%

A Survey: Particle Swarm Optimization-Based Algorithms for Grid Computing Scheduling Systems

Ambursa

Latip

2013

Journal of Computer Science

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Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Metatask-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category.

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A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow

Cited by 38 publications

References 36 publications

Optimum ship pilot plan model based on the modified discrete particle swarm optimization

Optimum ship pilot plan model based on the modified discrete particle swarm optimization

Using imperialist competition algorithm for independent task scheduling in grid computing

A Survey: Particle Swarm Optimization-Based Algorithms for Grid Computing Scheduling Systems

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