Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization

2018 ◽  
Vol 11 (4) ◽  
pp. 72-86
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das

Grid computing has been used as a new paradigm for solving large and complex scientific problems using resource sharing mechanism through many distributed administrative domains. One of the most challenging issues in computational Grid is efficient scheduling of jobs, because of distributed heterogeneous nature of resources. In other words, the job scheduling in computational Grid is an NP-hard problem. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. In this article, the authors propose a novel hybrid scheduling algorithm which combines intelligently the exploration ability of Particle Swarm Optimization (PSO) with the exploitation ability of Extremal Optimization (EO) which is a recently developed local-search heuristic method. The hybrid PSO-EO reduces the schedule makespan, processing cost, and job failure rate and improves resource utilization. The proposed hybrid algorithm is compared with the standard PSO, population-based EO (PEO) and standard Genetic Algorithm (GA) methods on all these parameters. The comparison results exhibit that the proposed algorithm outperforms other three algorithms.

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Weijie Xia ◽  
Xue Jin ◽  
Fawang Dou

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms.


Author(s):  
Yongbin Sun ◽  
Haibin Duan

Autonomous aerial refueling (AAR) is an essential application of unmanned aerial vehicles for both military and civilian domains. In this paper, a hybrid algorithm of the pigeon-inspired optimization (PIO) and lateral inhibition (LI), called LI-PIO, is proposed for image matching problem of AAR. LI is adopted for image pre-processing to enhance the edges and contrast of images. PIO, inspired from the homing characteristics of pigeons, is a novel bio-inspired swarm intelligence algorithm. To demonstrate the effectiveness and feasibility of our proposed algorithm, we make extensive comparative experiments with particle swarm optimization (PSO), particle swarm optimization based on lateral inhibition (LI-PSO), and PIO. It can be concluded from the experimental results that our proposed LI-PIO has excellent performances for image matching problem of AAR, especially in convergent rate and computation speed.


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