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- 저자 : Khurma Ruba Abu (검색결과 2 건)
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Khurma Ruba Abu,Alhenawi Esraa,Braik Malik,Hashim Fatma A,Chhabra Amit,Castillo Pedro A 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature Selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, i.e., they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, SO is employed to put the evolutionary algorithms’ preservation of the best premise into practice. This is accomplished by utilizing three alternative selection systems – tournament, proportional, and linear – to improve the exploration phase. These are used in exploration to allow solutions to be found more thoroughly and in relation to a chosen solution than at random. These are Tournament Logarithmic Snake Optimizer (TLSO), Proportional Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer. A number of 22 reference medical datasets were used in experiments. The findings indicate that, among 86% of the datasets, TLSO attained the best accuracy, and among 82% of the datasets, the best feature reduction. In terms of the standard deviation, the TLSO also attained noteworthy reliability and stability. On the basis of running duration, it is, nonetheless, quite effective.
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Hashim Fatma A,Mostafa Reham R,Khurma Ruba Abu,Qaddoura Raneem,Castillo Pedro A 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1
Sea horse optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named modified sea horse optimizer (mSHO). The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012 665, 2993.634, 0.01 266, 1.724 967, 263.8915, 0.032 255, 58 507.14, 1.339 956, and 0.23 524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-product batch plant, cantilever beam problem, and multiple disc clutch brake problems, respectively. Source codes of mSHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm.
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