Aquila Optimizer: A novel meta-heuristic optimization algorithm

Abualigah, Laith; Yousri, Dalia; Elaziz, Mohamed Abd; Ewees, Ahmed A.; Al-qaness, Mohammed A. A.; Gandomi, Amir H.

doi:10.1016/j.cie.2021.107250

Cited by 1,295 publications

(395 citation statements)

References 76 publications

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“…In this section, comprehensive experiments are conducted using three sets of optimization problems to test the performance of the proposed algorithms [4,7]. These problems are divided into three main parts; Part 1: Experiments on CEC 2015, which contains fifteen benchmark functions [66].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Shehab¹,

Abualigah

2021

Preprint

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Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

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Section: Experiments and Resultsmentioning

confidence: 99%

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Shehab¹,

Abualigah

2021

Preprint

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“…The proposed DAOA is analyzed and compared with six recently well-known algorithms, including Aquila Optimizer (AO) [30], Whale Optimization Algorithm (WOA) [53], Salp Swarm Algorithm (SSA) [54], Arithmetic Optimization Algorithm (AOA) [29], Particle Swarm Optimization (PSO) [55], Marine Predators Algorithm (MPA) [56], and Differential Evolution (DE) [57].…”

Section: Comparative Algorithms and Parameter Settingmentioning

confidence: 99%

“…Image segmentation has been tackled using a variety of approaches and algorithms [20]. Examples of the used optimization algorithms are the Bat Algorithm (BA) [21], Firefly Algorithm (FA) [22], Genetic Algorithm (GA) [23], Gray Wolf Optimizer (GWO) [24,25], Dragonfly Algorithm (DA) [26], Moth-Flame Optimization Algorithm (MFO) [27], Marine Predators Algorithm (MPA) [28], Arithmetic Optimization Algorithm (AOA) [29], Aquila Optimizer (AO) [30], Krill Herd Optimizer (KHO) [31], Harris Hawks Optimizer (HHO) [32], Red Fox Optimization Algorithm (RFOA) [33], Artificial Bee Colony Algorithm (ABC) [34], and Artificial Ecosystem-based Optimization [35]. Many other optimizers can be found in [36,37].…”

Section: Introductionmentioning

confidence: 99%

A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

et al. 2021

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One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

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“…Following [34], AO starts by determining the initial value for a set of N individuals X using the following formula:…”

Section: Aquila Optimizer (Ao)mentioning

confidence: 99%

“…One of the most efficient time series prediction models is the Adaptive Neuro Fuzzy Inference System (ANFIS), which was employed for different forecasting problems [31][32][33]. In this paper, we improve a modified ANFIS model using a new metaheuristic optimization algorithm called the Aquila Optimizer (AO) [34]. It belongs to a class of nature-inspired optimization algorithms, which are motivated by the behavior of living organisms, such as grey wolves [35], harris hawks [36], or red foxes [37].…”

Section: Introductionmentioning

confidence: 99%

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

et al. 2021

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Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

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Aquila Optimizer: A novel meta-heuristic optimization algorithm

Cited by 1,295 publications

References 76 publications

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

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