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tree seed algorithm-Based Optimized Deep Features Selection for Glaucoma Disease Classification

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第3期16卷 595-602页

作者： Amin, Sherif Tawfik Jazan Univ Coll Engn & Comp Sci Dept Comp Sci Jazan Saudi Arabia

Glaucoma is a common eye condition that can cause irreversible blindness if left untreated. Glaucoma can be identified by the optic nerve disorder (a perilous path that carries the potential risk) and leads to blindness. Therefore, early glaucoma detection is critical for optimizing treatment outcomes and preserving vision. The majority of afflicted people typically do not exhibit any overt symptoms. Since many afflicted people go untreated as a result, early detection is essential for successful therapy. Systems for detecting glaucoma have been developed through a great deal of research. These manual, time-consuming, and frequently erroneous traditional diagnostic methods are not suitable for glaucoma diagnosis thus, automated methods are required. This research study proposes a novel glaucoma diagnosis model that addresses the difficulty of determining the complex cup-to-disc ratio. For accurate feature extraction, a publicly available dataset with two classes (Glaucoma positive and negative) is utilized from Kaggle. The dataset is augmented using the Flip technique and resized. A two-step approach using the Mobilenetv2 model is used to extract features from positive and negative classes. Accurate features are selected with the help of Transfer Function Sequential Analysis (TSA). The enriched features are then classified using three different classifiers: Cubic SVM, Ensemble Subspace KNN, and Fine KNN. The experimental evaluation comprises 7 and 8 cross-validation folds. On 7 folds Ensemble Subspace KNN provides an accuracy of 97.33%, and on 8 folds Fine KNN provides the best accuracy of 97.92%.

关键词： Deep learning tree seed algorithm feature extraction mobilenetv2

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KATSA: KNN Ameliorated tree seed algorithm for complex optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Jiang, Jianhua Wu, Jiaqi Luo, Jinmeng Meng, Xianqiu Qian, Lize Li, Keqin Jilin Univ Finance & Econ Ctr Artificial Intelligence Changchun 130117 Peoples R China Jilin Univ Finance & Econ Jilin Prov Key Lab Fintech Changchun 130117 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jiangxi Acad Sci Inst Energy Res Nanchang 330096 Peoples R China State Univ New York New Paltz Dept Comp Sci New York NY 12561 USA

tree seed algorithm (TSA) is an outstanding algorithm for optimization problems, but it inevitably falls into the local optimum and has a low convergence speed in solving complex problems. This paper aims to address these above defects. Inspired by efficient learning from neighbors, a K-Nearest Neighbor (KNN) mechanism is adopted to enhance the tree or seed generation strategies for achieving the balance between exploitation and exploration. The proposed algorithm is named the KNN Ameliorated tree seed algorithm (KATSA). First, based on the current best tree, the search space is divided into best and non-best neighbor areas by the KNN mechanism. Based on this division approach, the proposed seed generation strategy has a precise heuristic, and the convergence speed can be accelerated. Second, the proposed seed generation and tree migration strategies integrate the proposed dynamic regulation mechanism, which reduces the possibility of falling into a local optimum. Third, the proposed feedback mechanism can effectively balance exploration and exploitation. With these enhancements from the KNN mechanism, KATSA can converge to the global optima more effectively during its evolutionary process. The results obtained from IEEE CEC 2014 benchmark function evaluation verify the excellent performance of the KATSA when compared with some recent variants, including STSA, EST-TSA, fb_TSA, and MTSA. In addition, GWO, PSO, BOA, BA, GA, LSHADE, and RSA are also adopted for some benchmark comparative experiments. The applicability of the proposed KATSA is demonstrated by three real complex and constrained problems when compared to TSA, fb_TSA, LSHADE, RSA, GWO, ABC, and PSO. The experimental results show that the proposed KATSA can obtain stable and optimal results on these complex problems. The source code is available at ***.

关键词： tree seed algorithm KNN KATSA Optimization problem Swarm intelligence

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Parameter extraction for photovoltaic models with tree seed algorithm

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ENERGY REPORTS 2023年 9卷 174-185页

作者： Beskirli, Ayse Dag, Idiris Eskisehir Osmangazi Univ Dept Comp Engn TR-26480 Eskisehir Turkiye Karamanoglu Mehmetbey Univ Dept Comp Engn TR-70200 Karaman Turkiye

Evaluation, simulation and optimization of PV systems is very important for fast and accurate parameter extraction based on current-voltage and power-voltage characteristic curves of photovoltaic models. Therefore, researchers used many metaheuristic algorithms to estimate the parameters of various PV modules. In this study, tree seed algorithm (TSA) was used for parameter estimation of the STM6-40/36 PV module. In the basic TSA, there are two different resolution mechanisms for balancing both local and global search technique. For this reason, basic TSA was preferred for parameter extraction of the PV module. The parameter results obtained by TSA were compared with those found by some other algorithms in the literature. According to the comparison result, the lowest root mean square error (RMSE) was obtained with the TSA algorithm. When the convergence graphs are examined, it is seen that TSA converges faster than other algorithms. When the box plots are analyzed, the results obtained by TSA have fewer outliers than the results of other algorithms, showing that TSA has a stable structure. In addition, a ranking graph was drawn according to the results obtained by all algorithms at each run time, and it was seen that TSA had the lowest RMSE value at all run times. Thus, it is concluded that TSA is a very competitive method for the PV module problem. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Photovoltaic models PV module model Parameter extraction I -V and P-V characteristic tree seed algorithm

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Enhanced Optimization of Shell Heavy Oil Fractionator using tree seed algorithm-based Decentralized PID Controller with Disk Margin Analysis 8

Enhanced Optimization of Shell Heavy Oil Fractionator using ...

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8th Conference on Advances in Control and Optimization of Dynamical Systems (ACODS)

作者： Govind, Achu K. R. Mahapatra, Subhasish Mahapatro, Soumya Ranjan VIT AP Univ Sch Elect Engn Amaravati Andhra Pradesh India Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India

This study focuses on optimizing the operation of a shell-heavy oil fractionator system through the utilization of a PID controller tuned with the tree seed algorithm (TSA). The primary objective is to improve system performance and robustness. The proposed approach considers essential factors including closed-loop gain, stability margins, and performance indices. Further, the controller is realized by imposing constraints on these factors. Robustness is further evaluated through a comprehensive disk margin analysis. The obtained simulation results demonstrate the remarkable efficacy of the TSA-tuned PID controller, showcasing substantial improvements in system performance, stability, and robustness within the complex shell-heavy oil fractionator system. This innovative methodology presents a promising avenue for advancing the field of industrial process optimization and control strategies.

关键词： Shell heavy oil fractionator PID controller tree seed algorithm Robustness Disk margin analysis Process control

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tree seed algorithm-based Feature Selection with Optimal Deep Learning Model for Supply Chain Management

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FLUCTUATION AND NOISE LETTERS 2024年第2期23卷 2440019-2440019页

作者： Alzahrani, Jaber S. Maashi, Mashael Alshahrani, Haya Mesfer Hassan, Abdulkhaleq Q. A. Khan, Jahangir Dutta, Ashit Kumar Eltahir, Yasir A. M. Saad, Hussam Eldin Hussein Gilkaramenthi, Rafiulla Umm Al Qura Univ Makkah Coll Engn Alqunfudah Dept Ind Engn Mecca 24211 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Software Engn POB 103786 Riyadh 11543 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Arts Mahayil Dept English Abha Saudi Arabia King Khalid Univ Appl Coll Mohyail Asir Dept Comp Sci Abha Saudi Arabia AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Riyadh 13713 Saudi Arabia AlMaarefa Univ Coll Appl Sci Dept Resp Care Riyadh 13713 Saudi Arabia AlMaarefa Univ Coll Appl Sci Dept Anesthesia Riyadh 13713 Saudi Arabia AlMaarefa Univ Coll Appl Sci Dept Emergency Med Serv Riyadh 13713 Saudi Arabia

In recent days, supply chain and logistic industries have been going through a transformational wave of automation and digitization. Supply chain management (SCM) can involve machine learning (ML) abilities and prediction models to ensure that the demands are satisfied at a minimum cost. Intelligent models can be developed to determine whether adequate inventory is accessible to encounter the predictable rise in demands, and if not, the system spontaneously begins to adjust the orders with suppliers to source the raw materials for resolving the predicted future high demand. The conventional ways of SCM can be replaced by the design of recent artificial intelligence (AI) and deep learning (DL) techniques. By this motivation, this research presents a tree seed algorithm-based feature selection with optimum DL technique for supply chain management (TSA-ODLSCM). The proposed TSA-ODLSCM model involves the design of a feature subset selection approach using a tree search algorithm (TSA) algorithm. Besides, a new convolutional neural network with fuzzy cognitive maps (CNN-FCM) technique is designed for the classification process. Moreover, optimal parameter tuning of the CNN-FCM model was performed using the Henry gas solubility optimization (HGSO) technique. To exhibit the improved performance of the TSA-ODLSCM approach, a huge range of simulations were executed and outcomes were examined below several aspects. The experimental validation reported an enhanced 96.52% outcome of the TSA-ODLSCM approach over other methods.

关键词： Supply chain management deep learning machine learning feature selection tree seed algorithm

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A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Beskirli, Ayse Dag, Idiris Kiran, Mustafa Servet Eskisehir Osmangazi Univ Dept Comp Engn Eskisehir Turkiye Konya Tech Univ Fac Comp & Informat Sci Konya Turkiye

tree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I)- voltage (V) and power (P)- voltage (V) charac- teristic curves obtained according to the experimen

关键词： PV module Photovoltaic models Parameter estimation tree seed algorithm Optimization

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Diagnosability-optimization design of fixed-wing unmanned aerial vehicle based on causal matching and tree seed algorithm

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MEASUREMENT & CONTROL 2024年

作者： Gu, Xuping Shi, Xianjun Naval Aeronaut Univ Coll Coastal Def Force 188 Erma Rd Yantai 264001 Shandong Peoples R China

With mission intensity and difficulty escalating, the future market demand for UAV reliability and safety grows dramatically. So, this paper proposes an optimal design strategy based on causal matching and the tree seed algorithm (TSA) based on structural analysis. First, a novel algorithm is designed for finding the minimum set of consistency relations for diagnosability analysis. The complexity of this algorithm is at a polynomial level, which is a significant improvement over previous algorithms with exponential complexity. A causal consistency search algorithm is also innovatively proposed for causal diagnosability analysis considering the causal constraints of the dynamic variables. Secondly, a diagnosability optimization strategy based on TSA is designed to balance the diagnosability requirements and the design cost of consistency relations. This strategy can satisfy the system's diagnosability demand under different causal constraints with minimum consistency relations. Finally, a fixed-wing UAV model is established to analyze the diagnosability under different causal constraints qualitatively. Based on the TSA, consistency relations with the minimum integrated diagnosis cost and the best diagnosis performance are preferred.

关键词： Fixed-wing UAV structural analysis causality tree seed algorithm diagnosability

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Enhanced Optimization of Shell Heavy Oil Fractionator using tree seed algorithm-based Decentralized PID Controller with Disk Margin Analysis

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IFAC-PapersOnLine 2024年 57卷 397-402页

作者： K.R Achu Govind Subhasish Mahapatra Soumya Ranjan Mahapatro School of Electronics Engineering VIT-AP Univerity Near Vijayawada Andhra Pradesh India School of Electronics Engineering Vellore Institute of Technology Chennai Tamil Nadu India

关键词： Shell heavy oil fractionator PID controller tree seed algorithm Robustness Disk margin analysis Process control

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A novel tree seed algorithm for optimal reactive power planning and reconfiguration based STATCOM devices and PV sources

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SN APPLIED SCIENCES 2021年第3期3卷 336页

作者： Belkacem, Mahdad Univ Biskra Biskra Algeria

Reactive power dispatch based on static synchronous compensator (STATCOM) and photovoltaic (PV) sources in coordination with reconfiguration strategy is vital task to ensure reliable operation of modern radial distribution (RD) systems. This paper introduces a novel tree seed algorithm (TSA) to improve the performance of modern RD power systems. A variant of TSA based on intelligent and flexible mechanism search is proposed to create an interactive equilibrium between exploration and exploitation during the search process to improve the global solution. Two objective functions, the total power loss and the margin reserve security are optimized under normal condition and critical load growth. To obtain reliable operation of the distribution system under critical situations, a reactive power dispatch based STATCOM devices is managed in coordination with the reconfiguration stage to find the best combination of sectionalizing switches and tie switches to be operated. The efficiency and particularity of the proposed distribution-planning strategy validated on two test systems, the radial 33 buses and the practical radial 250 buses (30 kV) of Sonelgaz Company located in Biskra in the south of Algeria. This radial electric network designed principally to deliver energy to several small and important agricultural consumers. Results obtained based on various test cases and scenarios using the proposed new planning strategy confirm the efficiency of the proposed planning strategy in terms of reduction of the total power loss, improvement voltage magnitude and enhancement of margin reserve.

关键词： Reconfiguration tree seed algorithm Radial distribution electric system Reactive power planning STATCOM Power loss PV source

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ATSA: An Adaptive tree seed algorithm based on double-layer framework with tree migration and seed intelligent generation

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KNOWLEDGE-BASED SYSTEMS 2023年 279卷

作者： Jiang, Jianhua Yang, Xi Li, Mengjuan Chen, Taibo Jilin Univ Finance & Econ Ctr Artificial Intelligence Changchun 130117 Peoples R China Jilin Univ Finance & Econ Jilin Prov Key Lab Fintech Changchun 130117 Peoples R China Jilin Univ Sch Business & Management Changchun 130012 Peoples R China

The tree seed algorithm (TSA) is a popular meta-heuristic algorithm that excels in solving optimization problems. However, TSA has some structural deficiencies and certain limitations manifested as limited population diversity, inadequate information utilization, and local optima stagnation. This paper proposes the Adaptive tree seed algorithm (ATSA) to solve these mentioned shortcomings with three enhancements. First, a double-layer framework is designed to achieve a more effective balance between exploration and exploitation with the feedback mechanism. Second, based on this framework, an evolutionary classifier is designed to generate seeds intelligently for enriching population diversity. Third, a migration mechanism is proposed to avoid falling into local optima. The performance of ATSA is tested by 30 benchmark functions on IEEE CEC 2017 in comparison with 15 algorithms (TSA, STSA, TSASC, fb_TSA, EST-TSA, GWO, PSO, ABC, BOA, RSA, HBA, SMA, HHO, INFO, RUN). In addition, 5 engineering design problems are evaluated to illustrate applicability. All experimental results demonstrate that the proposed ATSA significantly outperforms other algorithms, especially in solving high-dimensional and complex problems, as verified by the Wilcoxon Signed-Rank test. The outstanding performance of ATSA makes it a promising candidate for addressing challenges in the field of swarm intelligence.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic algorithm Swarm intelligence tree seed algorithm Migration mechanism Evolutionary classifier

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