A novel metaheuristic inspired by horned lizard defense tactics

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(3)

Published: Feb. 16, 2024

Abstract This paper introduces HLOA, a novel metaheuristic optimization algorithm that mathematically mimics crypsis, skin darkening or lightening, blood-squirting, and move-to-escape defense methods. In crypsis behavior, the lizard changes its color by becoming translucent to avoid detection predators. The horned can lighten darken skin, depending on whether not it needs decrease increase solar thermal gain. lightening strategy is modeled including stimulating hormone melanophore rate( $$\alpha$$ α -MHS) influences these changes. Further, move-to-evasion also described. lizard’s shooting blood mechanism, described as projectile motion, modeled. These strategies balance exploitation exploration mechanisms for local global search over solution space. HLOA performance benchmarked with sixty-three problems from literature, testbench provided in IEEE CEC- 2017 “Constrained Real-Parameter Optimization”, analyzed dimensions 10, 30, 50, 100, well functions CEC-06 2019 “100-Digit Challenge”. Moreover, three real-world constraint applications CEC2020 two engineering problems, multiple gravity assist optimal power flow problem, are studied. Wilcoxon Friedman statistics tests compare results against ten recent bio-inspired algorithms. shows provides most more effectively than competing At same time, test ranks first, n-dimensional analysis performs better constrained 50 100. source code free available https://www.mathworks.com/matlabcentral/fileexchange/159658-horned-lizard-optimization-algorithm-hloa .

Language: Английский

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Optimal truss design with MOHO: A multi-objective optimization perspective

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0308474 - e0308474

Published: Aug. 19, 2024

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The (HO) is novel meta-heuristic methodology draws inspiration from natural behaviour of hippos. HO built upon trinary-phase model incorporates mathematical representations crucial aspects Hippo's behaviour, including their movements aquatic environments, defense mechanisms against predators, and avoidance strategies. conceptual framework forms basis for developing multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions size constraints concerning stresses on individual sections constituent parts, these problems also involved competing objectives, such as reducing weight structure maximum nodal displacement. findings six popular methods were used compare results. Four industry-standard performance measures this comparison qualitative examination finest Pareto-front plots generated by each algorithm. average values obtained Friedman rank test analysis unequivocally showed MOHO outperformed other resolving significant quickly. In addition finding preserving more Pareto-optimal sets, recommended algorithm produced excellent convergence variance objective decision fields. demonstrated its potential navigating objectives through diversity analysis. Additionally, swarm effectively visualize MOHO's solution distribution across iterations, highlighting superior behaviour. Consequently, exhibits promise valuable method issues.

Language: Английский

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A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 94227 - 94251

Published: Jan. 1, 2023

The vast majority of today's data is collected and stored in enormous databases with a wide range characteristics that have little to do the overarching goal concept. Feature selection process choosing best features for classification problem, which improves classification's accuracy. considered multi-objective optimization problem two objectives: boosting accuracy while decreasing feature count. To efficiently handle process, we propose this paper novel algorithm inspired by behavior waterwheel plants when hunting their prey how they update locations throughout exploration exploitation processes. proposed referred as binary plant (bWWPA). In particular approach, search space well technique's mapping from continuous discrete spaces are both represented new model. Specifically, fitness cost functions factored into algorithm's evaluation modeled mathematically. assess performance algorithm, set extensive experiments were conducted evaluated terms 30 benchmark datasets include low, medium, high dimensional features. comparison other recent algorithms, experimental findings demonstrate bWWPAperforms better than competing algorithms. addition, statistical analysis performed one-way analysis-of-variance (ANOVA) Wilcoxon signed-rank tests examine differences between compared These experiments' results confirmed superiority effectiveness handling process.

Language: Английский

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BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 94094 - 94115

Published: Jan. 1, 2023

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. AOA uses the main arithmetic operators' distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes binary version of (BAOA) tackle feature selection problem classification. algorithm's search space converted from continuous one using sigmoid transfer function meet nature task. classifier method known wrapper-based approach K-Nearest Neighbors (KNN), find best possible solutions. study 18 datasets University California, Irvine (UCI) repository evaluate suggested performance. results demonstrate BAOA outperformed Binary Dragonfly (BDF), Particle Swarm (BPSO), Genetic (BGA), Cat (BCAT) when various performance metrics were used, including classification accuracy, selected features worst optimum fitness values.

Language: Английский

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Optimized Deep Learning for Potato Blight Detection Using the Waterwheel Plant Algorithm and Sine Cosine Algorithm

Potato Research, Journal Year: 2024, Volume and Issue: unknown

Published: May 28, 2024

Abstract Potato blight, sometimes referred to as late is a deadly disease that affects Solanaceae plants, including potato. The oomycete Phytophthora infestans causal agent, and it may seriously damage potato crops, lowering yields causing financial losses. To ensure food security reduce economic losses in agriculture, diseases must be identified. approach we have proposed our study provide reliable efficient solution improve blight classification accuracy. For this purpose, used the ResNet-50, GoogLeNet, AlexNet, VGG19Net pre-trained models. We AlexNet model for feature extraction, which produced best results. After selected features using ten optimization algorithms their binary format. Binary Waterwheel Plant Algorithm Sine Cosine (WWPASC) achieved results amongst algorithms, performed statistical analysis on features. Five machine learning models—Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), K -Nearest Neighbour (KNN)—were train chosen most accurate was MLP model. hyperparameters of were optimized (WWPASC). indicate suggested methodology (WWPASC-MLP) outperforms four other techniques, with accuracy 99.5%.

Language: Английский

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Techno-economic design of a renewable energy-based reverse osmosis desalination system for an industrial area in Algeria: The case of Adrar's oil refinery

Desalination, Journal Year: 2025, Volume and Issue: unknown, P. 118569 - 118569

Published: Jan. 1, 2025

Language: Английский

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Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm

Biomimetics, Journal Year: 2023, Volume and Issue: 8(3), P. 313 - 313

Published: July 16, 2023

The virus that causes monkeypox has been observed in Africa for several years, and it linked to the development of skin lesions. Public panic anxiety have resulted from deadly repercussions infections following COVID-19 pandemic. Rapid detection approaches are crucial since reached a pandemic level. This study's overarching goal is use metaheuristic optimization boost performance feature selection classification methods identify lesions as indicators event Deep learning transfer used extract necessary features. GoogLeNet network deep framework extraction. In addition, binary implementation dipper throated (DTO) algorithm selection. decision tree classifier then label selected set optimized using continuous version DTO improve accuracy. Various evaluation compare contrast proposed approach other competing metrics: accuracy, sensitivity, specificity,

Language: Английский

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Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Biomimetics, Journal Year: 2023, Volume and Issue: 8(3), P. 270 - 270

Published: June 26, 2023

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated type can be reduced early detection. Nonetheless, a skilled professional always necessary manually diagnose malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, inadequate training models. In paper, we developed novel computationally automated biological mechanism for categorizing breast cancer. Using optimization approach Advanced Al-Biruni Earth Radius (ABER) algorithm, boosting classification realized. stages framework include data augmentation, feature extraction using AlexNet transfer learning, optimized convolutional neural network (CNN). learning CNN improved accuracy when results are compared recent approaches. Two publicly available datasets utilized evaluate framework, average 97.95%. To ensure statistical significance difference between methodology, additional tests conducted, analysis variance (ANOVA) Wilcoxon, addition evaluating various metrics. these emphasized effectiveness methodology current methods.

Language: Английский

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Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 110, P. 77 - 98

Published: Oct. 7, 2024

Language: Английский

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Chaos-Enhanced Archimede Algorithm for Global Optimization of Real-World Engineering Problems and Signal Feature Extraction

Processes, Journal Year: 2024, Volume and Issue: 12(2), P. 406 - 406

Published: Feb. 18, 2024

Optimization algorithms play a crucial role in wide range of fields, from designing complex systems to solving mathematical and engineering problems. However, these frequently face major challenges, such as convergence local optima, which limits their ability find global, optimal solutions. To overcome it has become imperative explore more efficient approaches by incorporating chaotic maps within original algorithms. Incorporating variables into the search process offers notable advantages, including avoid minima, diversify search, accelerate toward In this study, we propose an improved Archimedean optimization algorithm called Chaotic_AO (CAO), based on use ten distinct replace pseudorandom sequences three essential components classical algorithm: initialization, density volume update, position update. This improvement aims achieve appropriate balance between exploitation exploration phases, offering greater likelihood discovering global CAO performance was extensively validated through groups The first group, made up twenty-three benchmark functions, served initial reference. Group 2 comprises problems: design welded beam, modeling spring subjected tension/compression stresses, planning pressurized tanks. Finally, third group problems is dedicated evaluating efficiency field signal reconstruction, well 2D 3D medical images. results obtained in-depth tests revealed reliability terms speeds, outstanding solution quality most cases studied.

Language: Английский

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