Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

Abualigah, Laith; Elaziz, Mohamed Abd; Khodadadi, Nima; Forestiero, Agostino; Jia, Heming; Gandomi, Amir H.

doi:10.1007/978-3-030-99079-4_19

Cited by 32 publications

(14 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we discuss the configuration and fog cloud for the proposed system. The parameters settings are given in Table 2 [ 42 , 43 , 44 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

View full text Add to dashboard Cite

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

show abstract

“…In this section, we discuss the configuration and fog cloud for the proposed system. The parameters settings are given in Table 2 [ 42 , 43 , 44 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…with features of biomedical signals [5]. This technique is effective for complex and large datasets, and a well-designed classifier training model combined with high-quality dataset features frequently generates unexpectedly good outcomes.…”

Section: Supervised Classification Is a Machine-learning Technique Th...mentioning

confidence: 99%

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

Gong¹,

Hase²

2023

Adv. technol. innov.

View full text Add to dashboard Cite

The classification accuracy of one-dimensional (1D) biomedical signals is limited due to the lack of independence of the extracted features. To address this shortcoming, the study applies a swarm intelligence algorithm based on plant root systems (PRSs) to feature engineering. Some basic features of 1D biomedical signals are integrated into a digitized soil, and a root matrix is generated from this digitized soil and the PRS algorithm. The PRS features are extracted from the root matrix and used to classify the basic features. Following classification with the same biomedical signals and classifier, the accuracy of the added PRS set is generally higher than that of the base set. The result shows that the proposed algorithm can expand the application of 1D biomedical signals to include more biomedical signals in classification tasks for clinical diagnosis.

show abstract

“…One of the better algorithms is the AO method, which Abualigah proposed in 2021 [9], because it is simple to build, has consistent performance, and few configurable parameters. Its strong optimization capabilities have helped with a variety of global optimization problems, including feature selection [10], vehicle route planning [11], and machine scheduling [12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

View full text Add to dashboard Cite

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

show abstract

Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

Cited by 32 publications

References 48 publications

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Contact Info

Product

Resources

About