AFOX: a new adaptive nature-inspired optimization algorithm

ALRahhal, Hosam; Jamous, Razan

doi:10.1007/s10462-023-10542-z

Cited by 13 publications

(4 citation statements)

References 80 publications

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“…Batch size choices depend on available computational resources and dataset size. These parameters should be fine-tuned and customized according to the specific task and dataset characteristics to achieve optimal model performance [25].…”

Section: Fox Optimized Lstm-based Augmented Language Model (Flstm-alm)mentioning

confidence: 99%

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Chassab,

Zakaria,

Tiun

2024

IEEE Access

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The computer-based Automated Essay Scoring (AES) system automatically marks or scores student replies by considering relevant criteria. The methodology, which systematically categorizes writing quality, can increase operational effectiveness in academic and major commercial institutions. To study the projected score, AES relies on extracting numerous aspects from the student's response, including grammatical and textural information. However, the recovered features may result in dimensionality reduction and a challenging-to-understand feature selection procedure. As the number of parameters rises, the model also demands a large cost for processing and training the data. However, these problems worsen the accuracy of score prediction as a whole and widen the gap between actual and anticipated results. This study suggested the Fox-optimized Long Short-Term Memory-based Augmented Language Model (FLSTM-ALM) as a solution to these problems for giving successful training to text features; the model uses an augmented learning paradigm. The retrieval score was then analyzed and generated using a neural knowledge encoder and retriever. The neural model successfully classifies the output based on this score. The best features are then chosen using the fox optimization algorithm based on the food-searching category. This choice of parameters solves the exploration and optimization issue with document classification. The performance of the optimized AES system was assessed using the two datasets, ASAP and ETS, and it demonstrated a high accuracy of 98.92% and a low error rate of 0.096%. Dimensionality reduction can thus be fixed by optimizing the FLSTM-ALM model with an appropriate meta-heuristic method, such as the FOX algorithm, which raises the predicted accuracy, recall, and f1 score for the AES model.

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Section: Fox Optimized Lstm-based Augmented Language Model (Flstm-alm)mentioning

confidence: 99%

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Chassab,

Zakaria,

Tiun

2024

IEEE Access

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“…A method imitating natural intelligence, including neural networks, evolutionary algorithms, fuzzy systems, and machine learning [11][12][13].…”

Section: Computational Intelligencementioning

confidence: 99%

Exploring Embodied Intelligence in Soft Robotics: A Review

Zhao,

Wu,

Wang

et al. 2024

Biomimetics

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Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.

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“…Among the notable algorithms in this category are Particle Swarm Optimization (PSO) 20 , Ant Colony Optimization (ACO) 21 , and Artificial Bee Colony (ABC) algorithm 22 , Tunicate Swarm Algorithm (TSA) 23 , Beluga Whale Optimization (BWO) 24 , Aphid-Ant Mutualism (AAM) 25 , artificial Jellyfish Search (JS) 26 , Spotted Hyena Optimizer (SHO) 27 , Honey Badger Algorithm (HBA) 28 , Mantis Search Algorithm (MSA) 29 , Nutcraker Optimization Algorithm (NOA) 30 , Manta Ray Foraging Optimization (MRFO) 31 , Orca Predation Algorithm (OPA) 32 , Yellow Saddle Goatfish (YSG) 33 , Hermit Crab Optimization Algorithm (HCOA) 34 , Cheetah Optimizer (CO) 35 , Walrus Optimization Algorithm (WaOA) 36 , Red-Tailed Hawk algorithm (RTH) 37 , Barnacles Mating Optimizer (BMO) 38 , Meerkat Optimization Algorithm (MOA) 39 , Snake Optimizer (SO) 40 , Grasshopper Optimization Algorithm (GOA) 41 , Social Spider Optimization (SSO) 42 , Whale Optimization Algorithm (WOA) 43 , Ant Lion Optimizer (ALO) 44 , Grey Wolf Optimizer (GWO) 45 , Marine Predators Algorithm (MPA) 46 ,Aquila Optimizer (AO) 47 , Mountain Gazelle Optimizer (MGO) 48 , Artificial Hummingbird Algorithm (AHA) 49 , African Vultures Optimization Algorithm (AVOA) 50 , Bonobo Optimizer (BO) 51 , Salp Swarm Algorithm (SSA) 52 , Harris Hawks Optimizer (HHO) 53 , Colony Predation Algorithm (CPA) 54 , Adaptive Fox Optimization (AFO) 55 , Slime Mould Algorithm (SMA) 56 , Spider…”

Section: Literature Reviewmentioning

confidence: 99%

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Amiri,

Hashjin,

Montazeri

et al. 2023

Preprint

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The novelty of this article lies in introducing a novel nonparametric metaheuristic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 132 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both local search and exploitation, as well as in global search and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. The performance of the HO consistently surpassed that of the top 3 algorithms in achieving optimal value, except for 29 functions. However, although it did not exhibit strong convergence in these 29 functions, the standard deviation for them was lower than the other investigated algorithms, illustrating its ability to manage the functions effectively. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The Wilcoxon signed test demonstrates that HO exhibits a notable and statistically significant advantage over the investigated algorithms in effectively addressing the optimization problems examined in this study.

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AFOX: a new adaptive nature-inspired optimization algorithm

Cited by 13 publications

References 80 publications

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

An Optimized LSTM-Based Augmented Language Model (FLSTM-ALM) Using Fox Algorithm for Automatic Essay Scoring Prediction

Exploring Embodied Intelligence in Soft Robotics: A Review

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

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