Cat hunting optimization algorithm: a novel optimization algorithm

Ghaedi, Azam; Bardsiri, Amid Khatibi; Shahbazzadeh, Mehdi Jafari

doi:10.1007/s12065-021-00668-w

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Evolutionary algorithms 2 [10], [11] are optimization algorithms obtained from the behavior of living organisms such as the behavior of birds in traveling [12], cats in searching for prey [13], or penguins in warming their bodies [14]. Apart from being based on the behavior of living things, the concept of evolutionary algorithms is also derived from natural phenomena such as the evolution of living things, water droplets, or symbiotic relationships between living 600 things.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Evolutionary Algorithm in Game – A Systematic Review

Armanto

Rosyid

Muladi

et al. 2023

KINETIK

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Research in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further.

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Section: Evolutionary Algorithmmentioning

confidence: 99%

Evolutionary Algorithm in Game – A Systematic Review

Armanto

Rosyid

Muladi

et al. 2023

KINETIK

View full text Add to dashboard Cite

show abstract

“…Step 1-Initialization Phase: The 𝑃 search agents (hummingbird and cats [13]) are initialized. The position of the search agents𝐻 𝑖 are initialized.…”

Section: Hybrid Optimization Model-cghfamentioning

confidence: 99%

An Ensemble-Of-Deep Learning Model with Optimally Selected Features for Osteoporosis Detection from Bone X-Ray Images

2022

IJIES

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Osteoporosis is a condition wherein bone tissue deteriorates and bone strength deteriorates. Over time, the disease that can lead bone to become more permeable and fragile makes it more prone to breaking. Early recognition of osteoporosis via an X-ray approach will enhance diagnosis and treatment practices, but it will also assist in preventing national economic loss via mass screening and awareness. A novel early osteoporosis diagnosis model is developed for X-ray images in this research work. The following four primary steps are used to construct a unique Osteoporosis detection model in this study: "(a) pre-processing, (b) feature extraction, (c) optimal feature selection, and (d) Osteoporosis detection". Gabor filtering (noise reduction) and histogram equalization are used to pre-process the obtained raw data (X-ray) (quality enhancement). Features such as "Active shape model (ASM), active appearance model (AAM), gray level co-occurrence matrix (GLCM), mean local gradient pattern (M-LGP), and improved median ternary pattern (I-MTP)" are recovered from the pre-processed data. Following that, a new hybrid optimization model chooses the best features from the retrieved features. The cat guided hummingbird foraging algorithm (CGHFA) is a conceptual combination of the basic artificial hummingbird algorithm (AHM) and the cat hunting optimization algorithm (CHOA). The deep learning classifiers in the Osteoporosis detection phase are trained using these ideally selected characteristics. The newly created ensemble-of-classifiers model is used to represent the osteoporosis diagnostic phase. "Quantum deep neural network (QDNN), improved deep convolution neural network (I-DCNN) and recurrent neural network (RNN)" are some of the deep learning classifiers that is employed here. All of these classifiers are trained using the optimal features available. The loss function of DCNN is improved via harmonic mean based cross-entropy function. The final detection performance will be calculated by combining the results obtained from all of these characteristics (by taking the mean). Finally, the effectiveness of the anticipated model is validated by a comparative examination. Accordingly, the detection accuracy attained by the proposed deep ensemble model +CGHFA at Learn_rate=60 is 90.7%, at Learn_rate=70 is 92.14%, at Learn_rate=80 is 93.482% and at Learn_rate=90 is 94.8%, which is higher than the existing models.

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A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates

Ashraf,

Draz

2024

Artif Intell Rev

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Since installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses, accurate and reliable modeling is heavily demanded to detect any design issue prior to the system establishment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs. Accordingly, due to their recent promising achievements, a tremendous number of metaheuristic optimizers (MHOs) have been utilized to handle this task. Hence, this effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-date MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary-based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with their results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individually in terms of its governing factors, besides, the simulation circumstances related to the studied SOFC test cases. Over and above, numerical simulations are also introduced for commercial SOFCs’ stacks to evaluate the proposed MHOs-based methodology. Moreover, the mathematical formulation of various assessment criteria is systematically presented. After all, some perspectives and observations are provided in the conclusion to pave the way for further analyses and innovations.

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Cat hunting optimization algorithm: a novel optimization algorithm

Cited by 9 publications

References 26 publications

Evolutionary Algorithm in Game – A Systematic Review

Evolutionary Algorithm in Game – A Systematic Review

An Ensemble-Of-Deep Learning Model with Optimally Selected Features for Osteoporosis Detection from Bone X-Ray Images

A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates

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