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Computer Science, Artificial Intelligence
Elnaz Pashaei et al.
Summary: This paper proposes a new wrapper feature selection method based on the chimp optimization algorithm for biomedical data classification. The experimental results demonstrate that the proposed method can effectively remove irrelevant features and improve classification accuracy.
NEURAL COMPUTING & APPLICATIONS
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Article
Computer Science, Artificial Intelligence
Laith Abualigah et al.
Summary: The paper introduces a novel nature-inspired meta-heuristic optimizer, RSA, based on the hunting behavior of crocodiles. Through implementing two main steps of crocodile behavior, RSA shows unique search methods compared to existing algorithms, and achieves better results in various test functions and engineering problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Absalom E. Ezugwu et al.
Summary: This study proposes a new nature-inspired metaheuristic algorithm called Prairie Dog Optimization (PDO), which mimics the behavior of prairie dogs in their natural habitat. The algorithm achieves exploration and exploitation through the prairie dogs' foraging and burrow build activities, as well as their specific responses to different sounds. Experimental results demonstrate that PDO outperforms other well-known population-based metaheuristic algorithms and is effective in estimating optimal solutions for both benchmark and real-world engineering design problems.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Information Science & Library Science
Pradipta Kumar Mishra et al.
Summary: This paper introduces a new technique for multilevel thresholding based on the whale optimizer and social group optimizer, and compares it with other optimization techniques.
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Dong Zhao et al.
Summary: The research introduced improvements such as horizontal crossover search and vertical crossover search to enhance the solution quality and convergence speed of the ACOR algorithm, forming the improved CCACO algorithm. Experimental results demonstrate that CCACO shows superior convergence speed and solution quality in image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Dong Zhao et al.
Summary: By enhancing the selection mechanism of the ACOR method and introducing random spare strategy and chaotic intensification strategy, the convergence speed and accuracy can be significantly improved, effectively avoiding local optima. Through a series of experiments, these improved methods demonstrate superior performance in problem-solving, and compared to other techniques, RCACO has a more reliable ability to step out of local optima.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Biomedical
Hu Tianqing et al.
Summary: This paper introduces a novel two-phase approach for real-time detection of COVID-19 using chest X-ray images, combining deep learning with extreme learning machines and improving results with the Chimp Optimization Algorithm. The proposed approach outperforms other comparative benchmarks in terms of accuracy and efficiency on two datasets, with low relative errors.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Information Systems
Mohammad Khishe et al.
Summary: The proposed weighted chimp optimization algorithm effectively tackles key challenges in large-scale numerical optimization problems, demonstrating superior performance in terms of convergence speed, avoiding local minima, exploration, and exploitation compared to existing methods.
Article
Computer Science, Information Systems
Bing Ma et al.
Summary: This paper proposes a novel series of SSA variants by combining SSA with improved Tent chaos mutation, Levy flights mutation, elite opposition-based learning mutation, variable radius mutation, and their combinations. The performance of these variants is evaluated on 31 benchmark test functions and real-world constrained engineering problems, demonstrating superior performance compared to state-of-the-art optimization algorithms.
Article
Computer Science, Information Systems
Liu Jianhua et al.
Summary: The paper introduces Circle chaos map and T-distribution variation into the original Sparrow search algorithm to improve global search ability and formulate search rules. Through test results and non-parametric tests, it is verified that the proposed algorithm has stronger global optimization ability and higher convergence precision compared with other algorithms.
Article
Robotics
Takuya Muto et al.
Summary: The GWOM algorithm introduces a momentum mechanism on top of GWO, increasing the probability of the wolf pack searching for better positions and aiding in avoiding local optima.
ARTIFICIAL LIFE AND ROBOTICS
(2021)
Article
Computer Science, Information Systems
Li Cao et al.
Summary: The study introduces an improved crow search algorithm to optimize the extreme learning machine, enhancing global search capability and gradually reducing search trajectory amplitude to avoid being attracted by local extremum, ultimately optimizing hidden layer neurons and connection weights for accurate prediction results.
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Computer Science, Artificial Intelligence
Zhikai Xing
KNOWLEDGE-BASED SYSTEMS
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Computer Science, Artificial Intelligence
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Computer Science, Artificial Intelligence
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Computer Science, Interdisciplinary Applications
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Computer Science, Artificial Intelligence
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Computer Science, Information Systems
Ujjwal Maulik
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Computer Science, Artificial Intelligence
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EXPERT SYSTEMS WITH APPLICATIONS
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Computer Science, Artificial Intelligence
D Feng et al.
PATTERN RECOGNITION LETTERS
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Neurosciences
YL Lu et al.
