Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling

Attiya, Ibrahim; Abualigah, Laith; Alshathri, Samah; Elsadek, Doaa; Elaziz, Mohamed Abd

doi:10.3390/math10111894

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…T 173-P130-R7-B6-L5-T14-C0.2 30 30 0 2184 optimal (10) T 173-P130-R6-B7-L5-T14-C0.2 15 15 0 2165 optimal (11) T 173-P150-R6-B9-L4-T14-C0.2 261 261 0 2095 optimal (12) T 173-P130-R5-B8-L5-T14-C0.1 134 134 0 2094 optimal (13) T 173-P170-R7-B6-L5-T14-C0.2 30 30 0 1655 optimal (14) T 173-P150-R6-B7-L5-T14-C0.2 166 166 0 1498 optimal (15) T 173-P170-R6-B7-L5-T14-C0.1 21 21 0 1450 optimal (16) T 173-P130-R6-B9-L4-T14-C0.3 61 61 0 1448 optimal (17) T 173-P130-R6-B9-L4-T14-C0.2 33 33 0 1381 optimal (18) T 173-P150-R9-B6-L4-T14-C0.1 22 22 0 1186 optimal (19) T 173-P150-R7-B6-L5-T14-C0.1 15 15 0 1111 optimal (20) T 173-P170-R6-B7-L5-T14-C0.2 52 52 0 1067 optimal (23) T 173-P170-R5-B8-L5-T14-C0.2 41 41 0 834 optimal (30) T 173-P150-R5-B8-L5-T14-C0.2 52 52 0 633 optimal (31) T 173-P170-R6-B9-L4-T14-C0.1 76 76 0 632 optimal (32) T 173-P130-R7-B6-L5-T14-C0.1 24 24 0 629 optimal (33) T 173-P170-R5-B8-L5-T14-C0.1 40 40 0 611 optimal (34)…”

Section: Gurobi Resultsmentioning

confidence: 99%

“…SA [16] is a well-known metaheuristic that has been used to solve many different problems such as routing problems [17][18][19][20], symbolic regression [21], feature selection and/or hyperparameter tuning for classification algorithms [22][23][24], influence maximization on social networks [25], and many other problems [26,27]. Furthermore, SA has been implemented for solving many different scheduling problems related to machine scheduling problems [28], scheduling of relief teams in natural disasters [29], for the multiobjective job-shop problem [30], in scheduling tasks in cloud computing applications [31], among others.…”

Section: Heuristic Approachmentioning

confidence: 99%

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A Track-Based Conference Scheduling Problem

et al. 2022

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The scheduling of conferences is a challenging task that aims at creating successful conference programs that fulfill an often wide variety of requirements. In this work, we focus on the problem of generating conference programs that organize talks into tracks: subevents within the conference that are group-related talks. The main contributions of this work can be organized into three scopes: literature review, problem formulation and benchmarking, and heuristic approach. We provide a literature review of conference scheduling approaches that organizes these approaches within a timetabling problem taxonomy. We also describe the main characteristics of the conference scheduling approaches in the literature and propose a classification scheme for such works. To study the scheduling of conferences that include tracks, we introduce the definition of the track-based conference scheduling problem, a new problem that incorporates tracks in the conference program. We provide a binary integer linear programming model formulation for this problem. Our formulation considers the availability of presenters, chairs, and organizers, the avoidance of parallel tracks, and best paper sessions, among other classical constraints of conference scheduling problems. Additionally, based on our formulation, we propose a simple instance-generation procedure that we apply to generate a set of artificial instances. We complete our work by proposing a heuristic method based on the simulated annealing metaheuristic for solving the track-based conference scheduling problem. We compare the results obtained by our heuristic approach and the Gurobi solver regarding execution time and solution quality. The results show that the proposed heuristic method is a practical approach for tackling the problem as it obtains solutions in a fraction of the time required by Gurobi, while Gurobi is also unable to obtain an optimal solution in the defined time for a subset of the instances. Finally, from a general perspective, this work provides a new conference scheduling problem formulation that can be extended in the future to include other features common in conference programs. Moreover, thanks to the instance generation procedure, this formulation can be used as a benchmark for designing and comparing new solving approaches.

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Section: Gurobi Resultsmentioning

confidence: 99%

Section: Heuristic Approachmentioning

confidence: 99%

A Track-Based Conference Scheduling Problem

et al. 2022

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“…Nevertheless, this particular methodology is exclusively applicable in a cloud environment and solely focuses on mitigating the makespan issue. In a recent publication by , a novel dynamic Jellyfish Search algorithm, DJSD, is developed 16 . In this work simulated annealing operators are integrated into the conventional Jellyfish Search Algorithm during the exploration phase in a competitive manner to improve diversity of the search.…”

Section: Review Of Related Workmentioning

confidence: 99%

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

Dankolo,

Radzi,

Mustaffa

et al. 2024

Baghdad Sci.J

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The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.

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“…Attiya et al 30 have contributed a Dynamic Simulated Annealing and Jellyfish Search Algorithm (DSAJSA) using a disruption operator for better TS with minimized makespan and degree of imbalance. This DSAJSA‐based TS process is proposed in a competitive manner for enhancing the potentiality of feasibly discovering more regions.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Nithiavathy,

Janakiraman,

Deva Priya

2023

Trans Emerging Tel Tech

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In a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

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Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling

Cited by 13 publications

References 33 publications

A Track-Based Conference Scheduling Problem

A Track-Based Conference Scheduling Problem

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

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