A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification

Applied Soft Computing, Год журнала: 2023, Номер 142, С. 110319 - 110319

Опубликована: Апрель 22, 2023

Язык: Английский

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EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications

Mathematics, Год журнала: 2023, Номер 11(4), С. 851 - 851

Опубликована: Фев. 7, 2023

The jellyfish search (JS) algorithm impersonates the foraging behavior of in ocean. It is a newly developed metaheuristic that solves complex and real-world optimization problems. global exploration capability robustness JS are strong, but still has significant development space for solving problems with high dimensions multiple local optima. Therefore, this study, an enhanced (EJS) developed, three improvements made: (i) By adding sine cosine learning factors strategy, can learn from both random individuals best individual during Type B motion swarm to enhance accelerate convergence speed. (ii) escape operator, skip trap optimization, thereby, exploitation ability algorithm. (iii) applying opposition-based quasi-opposition population distribution increased, strengthened, more diversified, better selected present new opposition solution participate next iteration, which solution’s quality, meanwhile, speed faster algorithm’s precision increased. In addition, performance EJS was compared those incomplete improved algorithms, some previously outstanding advanced methods were evaluated on CEC2019 test set as well six examples real engineering cases. results demonstrate increase calculation practical applications also verify its superiority effectiveness constrained unconstrained problems, therefore, suggests future possible such

Язык: Английский

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An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(5), С. 3133 - 3172

Опубликована: Фев. 24, 2023

Язык: Английский

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Memory-Based Sand Cat Swarm Optimization for Feature Selection in Medical Diagnosis

Electronics, Год журнала: 2023, Номер 12(9), С. 2042 - 2042

Опубликована: Апрель 28, 2023

The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential include excessive noisy, irrelevant, and redundant features. As a result, it is critical pick the most pertinent features classification task, which referred as Feature Selection (FS). Among FS approaches, wrapper methods are designed select appropriate subset In this study, two intelligent approaches implemented using new meta-heuristic algorithm called Sand Cat Swarm Optimizer (SCSO). First, binary version SCSO, known BSCSO, constructed by utilizing S-shaped transform function effectively manage nature in domain. However, BSCSO suffers from poor search strategy because has no internal memory maintain best location. Thus, will converge very quickly local optimum. Therefore, second proposed method devoted formulating an enhanced Binary Memory-based SCSO (BMSCSO). It integrated memory-based into position updating process exploit further preserve solutions. Twenty one benchmark disease datasets were used implement evaluate improved methods, BMSCSO. per results, BMSCSO acted better than terms fitness values, accuracy, number selected Based on obtained can efficiently explore feature domain optimal set.

Язык: Английский

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A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction

Measurement, Год журнала: 2023, Номер 221, С. 113525 - 113525

Опубликована: Сен. 3, 2023

Язык: Английский

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