An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism

Wang, Wan Liang; Li, Wei Kun; Wang, Yu Le

doi:10.1155/2019/5126239

Cited by 4 publications

(3 citation statements)

References 68 publications

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“…Timely and accurate diagnosis of thyroid disorders is pivotal for effective medical intervention and patient care [2]. With advancements in machine learning and optimization algorithms, the field of medical diagnostics has witnessed remarkable progress [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Thyroid Disease Diagnosis through Emperor Penguin Optimization Algorithm

Alsudani,

Nafea Saeea

2023

wjps

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The thyroid gland plays a pivotal role in maintaining overall health, making the accurate diagnosis of thyroid diseases crucial. In this study, we propose an innovative approach to enhance the diagnostic accuracy of thyroid diseases through the integration of the Emperor Penguin Optimization Algorithm (EPO). EPO, inspired by the efficient foraging behavior of emperor penguins, offers a unique optimization strategy for feature selection and model tuning in medical diagnostics. By employing EPO, we optimize the selection of relevant diagnostic features and fine-tune the parameters of machine learning models. Our experimental results demonstrate a significant improvement in diagnostic accuracy compared to traditional methods. The EPO-enhanced Thyroid Disease Diagnosis model achieves superior performance, ensuring a higher true positive rate and a lower false positive rate. This promising outcome suggests that EPO can be a valuable tool in the development of more accurate and reliable diagnostic systems for thyroid diseases, potentially leading to early detection and improved patient outcomes. Our approach achieved an impressive accuracy rate of 99.7 % when tested.

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Section: Introductionmentioning

confidence: 99%

Enhancing Thyroid Disease Diagnosis through Emperor Penguin Optimization Algorithm

Alsudani,

Nafea Saeea

2023

wjps

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“…It has been successfully applied in the target tracking system with high real-time requirements [3]. Firstly, the automatic programming optimized moving target vector graphics combination and resolution algorithm is applied to image filtering, segmentation, and target tracking [4]. An automatic programming and optimization algorithm of moving target vector graphics combination splitting and target tracking based on the color histogram is proposed [5].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Combination Resolution Algorithm for Moving Object Vector Graphics Based on Automatic Programming Optimization Algorithm

Zhang

Alexandrovich

Qiu

et al. 2022

Scientific Programming

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A new moving target adaptive image segmentation algorithm is proposed. On the basis of setting the adaptive tracking wave gate to track the moving target, the adjustment coefficient is adaptively calculated according to the criterion of maximum interclass variance function, and then, the adaptive segmentation of the image is completed by setting the threshold segmentation method. Compared with algorithm, iteration method, and maximum entropy method, the proposed algorithm can not only adapt to a variety of complex backgrounds but also has high precision and high speed. Therefore, it is a practical and effective image segmentation method. As a kind of nonparametric density estimation algorithm, automatic programming optimization algorithm of moving target vector graphic combination and resolution has been widely used in video moving target tracking. The algorithm has fast calculation efficiency and is not sensitive to target deformation and rotation; in the case of a partial shade, it has certain robustness. However, the algorithm moves too fast under clear conditions without considering the direction and speed of the target information, so it is easy to lose tracking when tracking fast moving targets. To solve this problem, a new method based on motion vector analysis and automatic programming optimization of moving target vector graphics combination resolution tracking algorithm is proposed. Firstly, the probability and statistics analysis of motion vectors generated in the process of video coding is carried out to obtain the estimated value of target motion direction and speed. Then, the center position of the candidate region can be split by correcting the combination of moving object vector graphics optimized by automatic programming so that the candidate center position is closer to the actual target center position at the beginning of each search. Compared with the traditional automatic programming optimization algorithm, the new algorithm not only improves the tracking accuracy of the fast-moving target but also reduces the number of search iterations, thus improving the operation efficiency. The algorithm can be applied to the case that video coding and target tracking are calculated simultaneously in the intelligent video surveillance equipment, and the experimental results show that the algorithm is effective and feasible.

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“…The second problem is that feature number and accuracy are two basic objectives, and other objectives are needed to explore the potential information to guide the evolution in feature selection [ 1 ]. Correspondingly, more objectives result in many-objective optimization problems (MaOPs) [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Based on a Large‐Scale Many‐Objective Evolutionary Algorithm

Sun

Liu

et al. 2021

Computational Intelligence and Neuroscience

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The feature selection problem is a fundamental issue in many research fields. In this paper, the feature selection problem is regarded as an optimization problem and addressed by utilizing a large-scale many-objective evolutionary algorithm. Considering the number of selected features, accuracy, relevance, redundancy, interclass distance, and intraclass distance, a large-scale many-objective feature selection model is constructed. It is difficult to optimize the large-scale many-objective feature selection optimization problem by using the traditional evolutionary algorithms. Therefore, this paper proposes a modified vector angle-based large-scale many-objective evolutionary algorithm (MALSMEA). The proposed algorithm uses polynomial mutation based on variable grouping instead of naive polynomial mutation to improve the efficiency of solving large-scale problems. And a novel worst-case solution replacement strategy using shift-based density estimation is used to replace the poor solution of two individuals with similar search directions to enhance convergence. The experimental results show that MALSMEA is competitive and can effectively optimize the proposed model.

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An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism

Cited by 4 publications

References 68 publications

Enhancing Thyroid Disease Diagnosis through Emperor Penguin Optimization Algorithm

Enhancing Thyroid Disease Diagnosis through Emperor Penguin Optimization Algorithm

An Adaptive Combination Resolution Algorithm for Moving Object Vector Graphics Based on Automatic Programming Optimization Algorithm

Feature Selection Based on a Large‐Scale Many‐Objective Evolutionary Algorithm

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