Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

Hashim, Fatma A.; Hussain, Kashif; Houssein, Essam H.; Mabrouk, Mai S.; Al-Atabany, Walid

doi:10.1007/s10489-020-01893-z

Cited by 705 publications

(294 citation statements)

References 47 publications

Supporting

Mentioning

292

Contrasting

Unclassified

Order By: Relevance

“…Equation 22 17and (18) to update transfer and density decreasing factor If 5 . 0  Tf then Use equation 20and (21) to update the acceleration and normalization Use equation (22) to update the position else If 5 .…”

Section: Updating Positionmentioning

confidence: 99%

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

Annrose

Rufus

Rex

et al. 2021

Preprint

View full text Add to dashboard Cite

Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

show abstract

Section: Updating Positionmentioning

confidence: 99%

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

Annrose

Rufus

Rex

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Real problems are solved more effectively since solutions are not restricted to locally optimal approaches. The metaheuristic algorithms are applied in various fields and proved helpful [8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…The metaheuristic algorithms are applied in various fields and proved helpful [9], [10]. Metaheuristics have proved to be useful in various problems, including this one [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

Self Cite

View full text Add to dashboard Cite

Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

show abstract

“…Moreover, the No-Free Lunch Theorem [8] states that no specific optimization algorithm can accurately solve multi-ple optimization problems. Thus, several MAs have been developed for use in biomedicine [9], [10], bioinformatics [11], [12], cheminformatics [13], [14], feature selection [15], engineering problems [16], [17], [18], [19], pattern recognition, text clustering [20], [21], and wireless sensor networks [22], [23]. However, all MAs need to balance exploration and exploitation stages; otherwise, solutions tend to become trapped in local optima or cannot properly converge [24], [25].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Tables (14, TSA-LEO OTSU) and (6, TSA-LEO Kapur) show the optimal thresholds obtained from TSA-LEO and other competitors under the condition of level = 2, 3, 4, and 5 for Otsu and Kapur objective functions. Tables(15,16,17,18, TSA-LEO Otsu) and(7, 8, 9, and 10, TSA-LEO Kapur) represent the fitness, PSNR, SSIM, and SSIM results, for Otsu and Kapur methods respectively. Moreover, Tables (19 and 19) show the results of the Wilcoxon rank-sum test of TSA-LEO and other seven algorithms with Otsu and Kapur methods.…”

mentioning

confidence: 99%

An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

et al. 2021

Self Cite

View full text Add to dashboard Cite

This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA-LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA-LEO was verified on the CEC'2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA-LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA-LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings. INDEX TERMSMetaheuristic algorithms; Tunicate swarm algorithm (TSA); Local escaping operator (LEO); Multilevel thresholding; Image segmentation; Kapur's entropy and Otsu method.

show abstract

Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

Cited by 705 publications

References 47 publications

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

Contact Info

Product

Resources

About