An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Ibrahim, Rehab Ali; Abualigah, Laith; Ewees, Ahmed A.; Al-qaness, Mohammed A. A.; Yousri, Dalia; Alshathri, Samah; Elaziz, Mohamed E. Abd

doi:10.3390/e23091189

Cited by 45 publications

(11 citation statements)

References 48 publications

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“…The objective of this study is to develop the state‐of‐the‐art method to build a flood susceptibility map with a high degree of accuracy, based on RBFNN and the arithmetic optimization algorithm (AOA). The AOA algorithm has been applied in several different fields, including economics and energy (Ibrahim et al, 2021; Khatir et al, 2021; Zheng et al, 2021). It has so far rarely been applied in environmental science or disaster management contexts.…”

Section: Introductionmentioning

confidence: 99%

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Nguyen

2022

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Flooding is the most dangerous of all environmental hazards and leads to significant impacts both on the environment and on human life. Environmental protection and water management can be achieved by modeling flood susceptibility, to inform strategy that will reduce flood damage. The aim of this study is to develop novel hybrid models based on a radial basis functions neural network (RBFNN), arithmetic optimization algorithm (AOA), artificial bee colony (ABC), and ant lion optimizer (ALO), to build flood susceptibility maps in the Quang Tri province of Vietnam. The obtained models were trained and validated using 1511 flood points and 14 conditioning factors. Various statistical indices were used to assess the performance of the models, namely root mean square error, receiver operation characteristics (ROC), area under the receiver operating characteristic curve (AUC), and the coefficient of determination (R2). The comparison analysis highlights that the RBFNN‐ABC model was better than other models with an AUC of 0.98, followed by RBFNN‐AOA, support vector machine, and random forest, all with an AUC value of 0.96, and finally RBFNN‐ALO, with an AUC of 0.95. From the flood susceptibility map, it can be seen that the eastern areas of the study area have high and very high flood susceptibility, that requires local government attention. The approach and results of this study can support national and local authorities, decision‐makers, and other planners in the construction of appropriate strategies to reduce potential damage in the future.

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Section: Introductionmentioning

confidence: 99%

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Nguyen

2022

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“…Recent research has focused on the application of metaheuristic algorithms to the cloud computing task scheduling problem in an attempt to discover a solution to these issues. One such technique is Electric Fish Optimization (EFO), a swarm intelligence-based application that models its search behavior after that of electric fish [10].…”

Section: Literature Surveymentioning

confidence: 99%

EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment

Kumar,

Kumar

2023

ICST Transactions on Scalable Information Systems

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The scheduling of tasks in the cloud is a major challenge for improving resource availability and decreasing the total execution time and energy consumption of operations. Due to its simplicity, efficiency, and effectiveness in identifying global optimums, electric fish optimisation (EFO) has recently garnered a lot of interest as a metaheuristic method for solving optimisation issues. In this study, we apply electric fish optimisation (EAEFA) to the problem of cloud task scheduling in an effort to cut down on power usage and turnaround time. The objective is to finish all tasks in the shortest possible time, or makespan, taking into account constraints like resource availability and task dependencies. In the EAEFA approach, a school of electric fish is used to solve a multi-objective optimization problem that represents the scheduling of tasks. Because electric fish are drawn to high-quality solutions and repelled by low-quality ones, the algorithm is able to converge to a global optimum. Experiments validate EAEFA's ability to solve the task scheduling issue in cloud computing. The suggested scheduling strategy was tested on HPC2N and other large-scale simulations of real-world workloads to measure its makespan time, energy efficiency and other performance metrics. Experimental results demonstrate that the proposed EAEFA method improves performance by more than 30% with respect to makespan time and more than 20% with respect to overall energy consumption compared to state-of-the-art methods.

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“…Metaheuristic algorithms are a subset of approximate algorithms that have been used for solving many NP-hard problems in different fields of science, such as engineering design [39][40][41][42][43][44][45][46][47][48][49][50], task scheduling [51][52][53], engineering prediction [54][55][56][57][58], and optimal power flow [59][60][61][62][63][64] problems. When tackling the FS problem, metaheuristic algorithms have shown outstanding results in prior studies [65][66][67][68]. For instance, Emary et al [69] introduced two versions of binary grey wolf optimizer (bGWO) to solve the FS problem as a wrapper-based method.…”

Section: Introductionmentioning

confidence: 99%

Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

et al. 2022

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Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accuracy and simultaneously minimize the number of selected features. Therefore, this paper is devoted to developing an efficient binary version of the quantum-based avian navigation optimizer algorithm (QANA) named BQANA, utilizing the scalability of the QANA to effectively select the optimal feature subset from high-dimensional medical datasets using two different approaches. In the first approach, several binary versions of the QANA are developed using S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to map the continuous solutions of the canonical QANA to binary ones. In the second approach, the QANA is mapped to binary space by converting each variable to 0 or 1 using a threshold. To evaluate the proposed algorithm, first, all binary versions of the QANA are assessed on different medical datasets with varied feature sizes, including Pima, HeartEW, Lymphography, SPECT Heart, PenglungEW, Parkinson, Colon, SRBCT, Leukemia, and Prostate tumor. The results show that the BQANA developed by the second approach is superior to other binary versions of the QANA to find the optimal feature subset from the medical datasets. Then, the BQANA was compared with nine well-known binary metaheuristic algorithms, and the results were statistically assessed using the Friedman test. The experimental and statistical results demonstrate that the proposed BQANA has merit for feature selection from medical datasets.

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An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Cited by 45 publications

References 48 publications

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

Flood susceptibility assessment using hybrid machine learning and remote sensing in Quang Tri province, Vietnam

EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment

Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

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