检索结果-内蒙古大学图书馆

Optimal Stochastic Process Optimizer: A New metaheuristic algorithm With Adaptive Exploration-Exploitation Property

IEEE ACCESS 2021年 9卷 108640-108664页

作者： Xu, Jiahong Xu, Lihong Tongji Univ Dept Elect & Informat Engn Shanghai 201804 Peoples R China

metaheuristic algorithms are constructed to solve optimization problems, but they cannot solve all the problems with best solutions. This work proposes a novel self-adaptive metaheuristic optimization algorithm, named Optimal Stochastic Process Optimizer (OSPO), which can solve different kinds of optimization problems with promising performance. Specifically, OSPO regards the procedure of optimization as a realization of stochastic process, and with the help of Subjective Probability Distribution Function (SPDF) and Receding Sampling Strategy proposed in this paper, OSPO can control the exploration-exploitation property online by the adaptive modification of the parameters in SPDF. This adaptive exploration-exploitation property of OSPO contributes to dealing with different kinds of problems;thus, it makes OSPO have the potential to solve at least a vast majority of optimization problems. The proposed algorithm is first benchmarked on uni-modal, multi-modal and composite test functions both in low and high dimensions. The results are verified by comparative studies with seven well-performed metaheuristic algorithms. Then, 21 real-world optimization problems are used to further investigate the effectiveness of OSPO. The winners of CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS algorithm, sCMAgES algorithm, EnMODE algorithm and COLSHADE algorithm are used as four comparative algorithms in real-world optimization problems. The analysis of simulations demonstrates that OSPO is able to provide very competitive performance compared to the comparative meta-heuristics both in benchmark functions and in real-world optimization problems;thus, the potential of OSPO to solve at least a vast majority of optimization problems is verified. A corresponding MATLAB APP demo is available on https://***/JiahongXu123/***.

关键词： Search problems Heuristic algorithms Space exploration Stochastic processes Measurement Diversity methods Benchmark testing Optimization metaheuristic algorithm benchmark exploration exploitation

来源：评论

学校读者我要写书评

暂无评论

Optimizing Hyperparameters for Improved Melanoma Classification using metaheuristic algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 531-540页

作者： Adamu, Shamsuddeen Alhussian, Hitham Aziz, Norshakirah Abdulkadir, Said Jadid Alwadin, Ayed Imam, Abdullahi Abubakar Garba, Aliyu Saidu, Yahaya Univ Teknol PETRONAS Comp & Informat Sci Dept Seri Iskandar Malaysia Ahmadu Bello Univ IAIICT Zaria Nigeria Univ Brunei Darussalam UBD Brunei Gadong Brunei King Saud Univ Comp Sci Dept Community Coll Riyadh Saudi Arabia

Melanoma a prevalent and formidable skin cancer, necessitates early detection for improved survival rates. The rising incidence of melanoma poses significant challenges to healthcare systems worldwide. While deep neural networks offer the potential for precise melanoma classification, the optimization of hyperparameters remains a major obstacle. This paper introduces a groundbreaking approach that harnesses the Manta Rays Foraging Optimizer (MRFO) to empower melanoma classification. MRFO efficiently fine-tunes hyperparameters for a Convolutional Neural Network (CNN) using the ISIC 2019 dataset, which comprises 776 images (438 melanoma, 338 non -melanoma). The proposed cost-effective DenseNet121 model surpasses other optimization methods in various metrics during training, testing, and validation. It achieves an impressive accuracy of 99.26%, an AUC of 99.56%, an F1 score of 0.9091, a precision of 94.06%, and a recall of 87.96%. Comparative analysis with EfficientB1, EfficientB7, EfficientNetV2B0, NesNetLarge, ResNet50, VGG16, and VGG19 models demonstrates its superiority. These findings underscore the potential of the novel MRFO-based approach in achieving superior accuracy for melanoma classification. The proposed method has the potential to be a valuable tool for early detection and improved patient outcomes.

关键词： Deep learning machine learning classification metaheuristic algorithm CNN

来源：评论

学校读者我要写书评

暂无评论

metaheuristic algorithm-Based Vibration Response Model for a Gas Microturbine

引用

SENSORS 2022年第12期22卷 4317-4317页

作者： Montoya-Santiyanes, L. A. Rodriguez-Abreo, Omar Rodriguez, Eloy E. Rodriguez-Resendiz, Juvenal Univ Politecn Queretaro Ind Technol Div Carretera Estatal 420 El Marques 76240 Mexico Red Invest OAC Optimizac Automatizac & Control El Marques 76240 Mexico Ctr Ingn & Desarrollo Ind CIDESI Queretaro 76125 Mexico Univ Autonoma Queretaro Fac Ingn Queretaro 76010 Mexico

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 degrees C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

关键词： microturbine vibration analysis metaheuristic algorithm response surface

来源：评论

学校读者我要写书评

暂无评论

A New metaheuristic algorithm Based on Shark Smell Optimization

引用

COMPLEXITY 2016年第5期21卷 97-116页

作者： Abedinia, Oveis Amjady, Nima Ghasemi, Ali Semnan Univ Dept Elect Engn Semnan Iran Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real-world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm. (C) 2014 Wiley Periodicals, Inc.

关键词： shark smell optimization metaheuristic algorithm optimization problem

来源：评论

学校读者我要写书评

暂无评论

metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks

引用

MICROPROCESSORS AND MICROSYSTEMS 2023年第1期101卷

作者： Prakash, Ved Pandey, Suman AKTU KNIT Sultanpur Lucknow India

The utilization of wireless sensor networks (WSNs), which combine sensing, communication, and computation in an intelligent environment. The use of energy becomes the primary obstacle for sensor nodes to overcome. Clustering and routing algorithms are promising ways to fix the issue and prolong the network's effectiveness. The availability of restricted energy resources is the most significant challenge in WSNs. In this paper, we propose an energy-efficient clustering scheme integrated with a particle swarm optimization method called PSO-EECS for wireless sensor networks to raise energy efficiency while simultaneously increasing the lifetime of sensor nodes. The predominant concern being addressed in this paper is the selection of a Cluster Head (CH), which helps gather, aggregate and forward the data from the cluster-based routing paradigm. PSO-EECS algo-rithm is used to optimize the fitness parameters for CH selection, which include the energy ratio, distance considerations, node density, load balancing, and the Network's average energy, among other things. The simulation results reveal that the proposed technique offers a considerable improvement in network stability and operation time compared to the currently used approaches. Compared to the PSOGSA, GAPSO-H, and ABE-DE protocol, simulation findings demonstrate that PSO-EECS enhances stability by 95.4%, 34.8%, and 29.2%, respectively.

关键词： WSNs PSO-EECS Clustering scheme metaheuristic algorithm Cluster head

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Time Management Skills on Academic Achievement with an XGBC Model and metaheuristic algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 107-119页

作者： Li, Songyang Criminal Invest Police Univ China Police Skills & Tact Training Dept Shenyang 110035 Liaoning Peoples R China

Estimating a student's academic performance is a crucial aspect of learning preparation. In order to predict understudy academic performance, this consideration uses a few Machine Learning (ML) models and Time Administration Aptitudes data from the Time Structure Questionnaire (TSQ). While a number of other useful characteristics have been used to forecast academic achievement, TSQ findings, which directly evaluate students' time management skills, have never been included. This oversight is surprising, as time management skills likely play a significant role in academic success. Time administration may be an ability that may impact the student's academic accomplishment. The purpose of this research is to look at the connection between college students' academic success and their ability to manage their time well. The Extreme Gradient Boosting Classification (XGBC) model has been utilized in this study to forecast academic student performance. To enhance the prediction accuracy of the XGBC model, this study employed three optimizers: Giant Trevally Optimizer (GTO), Bald Eagle Search Optimization (BESO), and Seagull Optimization algorithm (SOA). Impartial performance evaluators were employed in this study to assess the models' predictions, minimizing potential biases. The findings showcase the success of this approach in developing an accurate predictive model for student academic performance. Notably, the XGBE surpassed other models, achieving impressive accuracy and precision values of 0.920 and 0.923 during the training phase.

关键词： Student academic performance time management machine learning extreme gradient boosting classification metaheuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

A new improved Newton metaheuristic algorithm for solving mathematical and structural optimization problems

引用

EVOLUTIONARY INTELLIGENCE 2024年第4期17卷 2749-2789页

作者： Amiri, Ahmad Torkzadeh, Peyman Salajegheh, Eysa Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

As the objective function in complex structural optimization problems is implicit, in the present improved Newton method using the higher-order approximation, a new and simple formulation is proposed to calculate the first and second-order derivatives of the objective function. Furthermore, by combining the presented improved Newton method with an innovative metaheuristic algorithm an efficient approach, called improved Newton metaheuristic algorithm (INMA), is presented to balance between exploration and exploitation during optimization process. The performance of the proposed INMA algorithm is investigated on the CEC2019 and CEC2020 benchmark functions, three benchmark truss problems with stress constraints as well as four truss problems with multiple frequency constraints. The results demonstrate that the efficiency of the proposed INMA algorithm for both mathematical and structural optimization problems is better than other algorithms. Anyway, it can be seen from the results that INMA algorithm is an efficient method to solve problems in different optimization domains.

关键词： metaheuristic algorithm Improved Newton method Structural optimization Truss problem

来源：评论

学校读者我要写书评

暂无评论

Application of metaheuristic algorithm in intelligent logistics scheduling and environmental sustainability

引用

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 1727-1740页

作者： Jin, Xin Kunming Univ Sch Econ & Management Kunming Yunnan Peoples R China

In response to the problems of low efficiency, high cost, and serious environmental pollution faced by traditional logistics scheduling methods, this article introduced the metaheuristic algorithm into intelligent logistics scheduling and environmentally sustainable development. This article took the metaheuristic algorithm as the research object. It was based on an in-depth analysis of its core ideas and unique advantages, combined intelligent logistics scheduling with relevant theories and methods such as green environmental protection, and innovatively constructed an intelligent logistics scheduling model based on the metaheuristic algorithm. This article experimentally compared the effects of different metaheuristic algorithms on total driving distance, transportation time, fuel consumption, and carbon emissions. The experimental findings indicated that the ant colony optimization (ACO) algorithm in this article performed the best among them, and the performance of traditional algorithms and metaheuristic algorithms was also tested in terms of performance. The findings indicated that the computational accuracy of the metaheuristic algorithm reached 97%, which was better than the traditional 80%. Experimental results have shown that the metaheuristic algorithm is an efficient and feasible method that can improve the efficiency of logistics scheduling and environmental sustainability.

关键词： metaheuristic algorithm intelligent logistics scheduling environmental sustainability path optimization performance test

来源：评论

学校读者我要写书评

暂无评论

Literature Research Optimizer: A New Human-Based metaheuristic algorithm for Optimization Problems

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12817-12865页

作者： Ni, Lei Ping, Yan Yao, Na Jiao, Jiao Wang, Geng Southwest Univ Sci & Technol Key Lab Testing Technol Mfg Proc Minist Educ Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Tianfu Inst Res & Innovat Chengdu 610299 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 610054 Peoples R China

Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems.

关键词： Literature research optimizer metaheuristic algorithm Human-based Optimization Real-world problems

来源：评论

学校读者我要写书评

暂无评论

Ebola Optimization Search algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Ebola Optimization Search Algorithm: A new nature-inspired m...

引用

IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Univ KwaZulu Natal Sch Math Stat & Comp Sci Pietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

ISBN: (纸本)9781665442312

Deriving computational solutions to address complex life problems is now a gaining research focus. This is because such a computational approach is capable of modeling algorithms after natural phenomenon to find an optimal solution. In this paper, a nature-inspired and biology-based algorithm was inspired by the natural and biological process of Ebola. We formulated the propagation of the disease using a mathematical and SIR-model. Thereafter, an algorithmic design describing the procedure for the optimization process was done. The resulting Ebola optimization search algorithm (EOSA) was evaluated using the popular IEEE CEC functions' benchmark functions. The outcome of exhaustive experimentation with EOSA showed that the metaheuristic algorithm achieved a state-of-the-art performance compared with similar algorithms. Results confirmed that the scalability analysis, convergence analysis, and sensitivity analysis were competitive when compared with evolutionary-based and swarm-based algorithms.

关键词： complex problems metaheuristic algorithm optimization problems benchmark functions Ebola virus

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：