A New Task Scheduling Algorithm using Firefly and Simulated Annealing Algorithms in Cloud Computing

Fanian, Fakhrosadat; Bardsiri, Vahid Khatibi; Shokouhifar, Mohammad

doi:10.14569/ijacsa.2018.090228

Cited by 21 publications

(15 citation statements)

References 22 publications

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“…This means, the initial population that was employed in the SA algorithm is not randomly chosen but obtains the value that is provided to it by the FA algorithm. This is the actual optimum value that has been given by the FA algorithm [4].…”

Section: Hybrid Optimization Methodsmentioning

confidence: 99%

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Karpagam

2021

Preprint

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An inevitable part of the cloud computing environment is virtualization, as it can multiplex or combine many virtual machines in a single physical machine, and simultaneously an isolated environment is provided to every virtual machine. An important issue in cloud computing is workflow scheduling, which maps tasks of workflow to VMs based on various functional and non-functional requisites. Workflow scheduling is an NP-hard optimization problem and it is quite hard to achieve an optimal schedule. Metaheuristic algorithms helped in solving the problem of cloud task scheduling and this was compared to other heuristics. Reactive Search (RSO) and its structure will consist of a local heuristic based on a certain neighborhood complemented by making use of a memory-based mechanism. The Shuffled Frog Leaping Algorithm (SFLA) is based on swarm evolution that imitates information exchange divided into memeplexes when searching for food. This paper proposes a new set of optimization heuristics along with hybrid optimizations (RSO - SFLA) to solve problems in combinatorial optimization.

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Section: Hybrid Optimization Methodsmentioning

confidence: 99%

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Karpagam

2021

Preprint

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“…Heu-ristic algorithms cannot usually produce an optimal solution, as they don't explore the whole search space, nonetheless it may yield locally optimal solutions in a reasonable amount of time [34]. However, metaheuristic algorithms can provide a more robust solution at the expense of increased computational efforts for globally searching among the search space [35]. Since genetic algorithm (GA) has proved its efficiency in solving different supply chain problems in different studies with a satisfactory performance [36,37,38,39], we utilize GA to solve the proposed model for sustainable coal supply chain management including the economic and exergetic objectives.…”

Section: Solution Methods Based On Genetic Algorithmmentioning

confidence: 99%

Sustainable Coal Supply Chain Management Using Exergy Analysis and Genetic Algorithm

Naderi

Nikabadi

Alem-Tabriz

et al. 2020

Management Systems in Production Engineering

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Environmental threats of coal usage in the electricity production combined with the consumption of renewable and non-renewable resources had led to worldwide energy challenges. The cost of coal mining and economical and environmentally sustainable usage of mined coal could be optimized by efficient management of coal supply chain. This paper provides a mathematical model for improving coal supply chain sustainability including the cost of exergy destruction (entropy). In the proposed method, exergy analysis is used to formulate the model considering not only economic costs but also destructed exergy cost, while genetic algorithm is applied to efficiently solve the proposed model. In order to validate the proposed methodology, some numerical examples of coal supply chains are presented and discussed to show the usability of the proposed exergetic coal supply chain model and claim its benefits over the existing models. According to the results, the proposed method provides 17.6% saving in the consumed exergy by accepting 2.7% more economic costs. The presented model can be used to improve the sustainability of coal supply chain for either designing new projects or upgrading existing processes.

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“…Managing tasks in cloud computing environment is huge and daunting and it requires smart handling. Therefore, the study by Fakhrosadat Fanian et al, 15 has contributed towards a better way of managing the scheduling of tasks by developing a new algorithmic approach that takes its inspiration from Firefly and SA algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

et al. 2021

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Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

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A New Task Scheduling Algorithm using Firefly and Simulated Annealing Algorithms in Cloud Computing

Cited by 21 publications

References 22 publications

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

Sustainable Coal Supply Chain Management Using Exergy Analysis and Genetic Algorithm

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

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