Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization

Vairam, T; Sarathambekai, S.; Umamaheswari, K.

doi:10.1007/s12046-018-0984-x

Cited by 7 publications

(5 citation statements)

References 19 publications

(47 reference statements)

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“…The CSGOA-RS technique derives a fitness function and is utilized for testing the quality of the solution [24]. The FF consist of the tri-objectives such as the RC, the MS, and the MFT.…”

Section: Application Of Csgoa Technique For Resource Schedulingmentioning

confidence: 99%

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

P¹,

Ramabalan²

2022

Computer Systems Science and Engineering

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The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently. The distributed robotic control system can be used in real time for resolving various challenges such as localization, motion controlling, mapping, route planning, etc. The distributed robotic control system can manage different kinds of heterogenous devices. Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements. For instance, scheduling of resources (such as communication channel, computation unit, robot chassis, or sensor input) to the various system components turns out to be an essential requirement for completing the tasks on time. Therefore, resource scheduling is necessary for ensuring effective execution. In this regard, this paper introduces a novel chaotic shell game optimization algorithm (CSGOA) for resource scheduling, known as the CSGOA-RS technique for the distributed robotic control system environment. The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance. The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased. The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations. For validating the enhanced performance of the CSGOA-RS technique, a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures. The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.

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“…The CSGOA-RS technique derives a fitness function and is utilized for testing the quality of the solution [24]. The FF consist of the tri-objectives such as the RC, the MS, and the MFT.…”

Section: Application Of Csgoa Technique For Resource Schedulingmentioning

confidence: 99%

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

P¹,

Ramabalan²

2022

Computer Systems Science and Engineering

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“…Particle consist of the tasks and the mapped resources. Fitness function determines the effectiveness of the schedule [107]. Each particle is similar to chromosomes in genetic algorithm and have a fitness value, which will be assessed by a fitness capacity that need to be enhanced in each iteration [49] [79].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Singh

Tyagi

Kumar

et al. 2021

Simulation Modelling Practice and Theory

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“…e parameters of DPSO algorithm, part of DPSO-PDM algorithm, are the same as those of DPSO-TS algorithm, and the other parameters are the same as those in the article written by Li et al [13]. e parameters of the DPSO algorithm part of CHIDPSO algorithm are the same as those of DPSO-TS algorithm, and the other parameters are the same as those in the article written by Vairam et al [17]. Tables 4-7, and the best results can be obtained at these maneuvering times.…”

Section: Simulationsmentioning

confidence: 99%

“…Wang et al [16] propose SA-DPSO algorithm which combines the DPSO algorithm and simulated annealing algorithm. Vairam et al [17] propose CHIDPSO algorithm by combining DPSO algorithm and cyber swarm algorithm, which improves the local search capacity of DPSO algorithm. Vasudevan and Sinha [18] proposed a hybrid optimization algorithm by combining GA and PSO.…”

Section: Introductionmentioning

confidence: 99%

Fuel-Efficient on-Orbit Service Vehicle Allocation Based on an Improved Discrete Particle Swarm Optimization Algorithm

Liu

et al. 2020

Mathematical Problems in Engineering

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Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient transfer trajectory. This fuel efficiency maximization can then serve as the performance index of the OSV allocation optimization model problem. In particular, the maximization of the minimum residual fuel over individual OSVs is proposed as a performance index for OSV allocation optimization. The optimization problem is numerically solved through the proposed DPSO-TS algorithm. Finally, the simulation results demonstrate that the DPSO-TS algorithm has a higher accuracy compared to the DPSO, the DPSO-PDM and the DPSO-CSA algorithms in the premise that these four algorithms have the basically same computational time. The DPSO-TS algorithm can effectively solve the OSV allocation optimization problem.

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Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization

Cited by 7 publications

References 19 publications

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Fuel-Efficient on-Orbit Service Vehicle Allocation Based on an Improved Discrete Particle Swarm Optimization Algorithm

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