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An inverse reinforcement learning framework with the Q-learning mechanism for the metaheuristic algorithm

KNOWLEDGE-BASED SYSTEMS 2023年第1期265卷

作者： Zhao, Fuqing Wang, Qiaoyun Wang, Ling Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

A reward function is learned from the expert examples by inverse reinforcement learning (IRL), which is more reliable than an artificial method. The moth-flame optimization algorithm (MFO), which is based on the navigation mechanism of a moth flying at night, has been extensively employed to address the complex optimization problem. An inverse reinforcement learning framework with the Q-learning mechanism (IRLMFO) is designed to strengthen the performance of the MFO algorithm in a large-scale real-parameter optimization problem. The right strategy is chosen by the Q-learning mechanism, using historical data provided by the relevant approach in the strategy pool, which stores strategies that include diverse functions. The competition mechanism is designed to strengthen the exploitation capability of the IRLMFO algorithm. The performance of the IRLMFO is verified on the benchmark test suite in CEC 2017. Experimental results illustrate that the IRLMFO outperforms state-of-the-art algorithms. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Inverse reinforcement learning (IRL) Q-learning metaheuristic algorithm Moth-flame optimization algorithm (MFO) Competition mechanism

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Optimal design of the neutron multiplicity counter based on metaheuristic algorithm

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FRONTIERS IN PHYSICS 2023年 11卷

作者： Lu, Yunfeng Zhang, Quanhu Yao, Qingxu Wang, Yu Xian Res Inst High Technol Xian Shaanxi Peoples R China

The neutron multiplicity measurement and analysis method is a very important measurement and analysis method in the field of international arms control verification and nuclear safeguards, and its measurement accuracy depends to some extent on detector performance. In this paper, several newer current metaheuristic algorithms are introduced to achieve neutron multiplicity counter layout optimization by combining with MC (Monte Carlo) simulation software. The algorithm with the best finding ability is selected for the optimization of another detector layout. Based on the optimization results, k-means clustering analysis is introduced so that the optimized structure can be applied to neutron multiplicity measurements. The results show that the metaheuristic algorithm has good application capability for neutron detector layout optimization and can be further investigated.

关键词： neutron multiplicity detector layout optimization cluster analysis MC simulation metaheuristic algorithm

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

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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

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Seismic Optimization of Steel Mega-Braced Frame With Improved Prairie Dog metaheuristic Optimization algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2025年第3期34卷

作者： Payamifar, Tohid Sojoudizadeh, Reza Azizian, Hadi Rahimi, Loghman Islamic Azad Univ Dept Civil Engn Mahabad Branch Mahabad Iran

A mega-braced frame is one of the well-known structural systems used in tall buildings to resist lateral forces, which is composed of networks of diagonal braces and structural elements strategically placed within the building's frame to enhance its stiffness and stability. Determining the optimal angle of mega bracing in buildings presents challenges due to complex structural considerations, such as lateral load distribution, ensuring compatibility with building geometry and layout, and optimizing the overall structural efficiency while maintaining seismic integrity. For this purpose, a novel approach is proposed in this study for seismic optimization of steel mega-braced frames by employing an enhanced version of the prairie dog optimization (PDO) algorithm as one of the recently proposed metaheuristic algorithms. The new improved version of this algorithm called I-PDO is developed based the Levy flight concept, whereas the conventional Brownian randomization is replaced by Levy flight in the main search loop of the algorithm. For numerical investigations, a 10-story, 1-bay, and a 24-story, 5-bay, frame structures are considered. An optimization problem is developed based on the topology optimization of the mega bracing system, whereas a size optimization is also conducted for optimal determination of the design sections for structural elements. For comparative investigations, some of the well-known metaheuristics are also used for performance evaluation of the I-PDO in dealing with the structural optimization problems. The results demonstrate the capability of the I-PDO in providing better optimization results in dealing with the topology and size optimization of the mega-braced frame structures.

关键词： improved prairie dog optimization mega-braced frame structure metaheuristic algorithm seismic optimization

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A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles (vol 2024, e12383, 2024)

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JOURNAL OF ENGINEERING-JOE 2024年第6期2024卷

作者： Mishra, D. Maharana, M. K. Kar, M. K. Nayak, A. Islam, M. M. Ustun, T. S. KIIT Deemed be Univ Bhubaneswar Odisha India

Modern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization *** fluctuation due to RES intermittency, the proposed approach increases inertia and stability

关键词： power generation control distributed power generation synchronous generators electric vehicle charging Modified Differential Evolution method RESs island microgrid frequency control nonconventional sources modern electrical networks dynamic security electric vehicle Proportional Integral Derivative Filter Constant controller MDE-based PIDFN controller reducing frequency disturbances sharp rise virtual inertia control loop frequency control technique solar power stations reference frequency tracking diverse operational scenarios traditional synchronous generators power grids evolutionary computation metaheuristic algorithm standalone microgrid wind power plants

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Optimizing Hyperparameters for Improved Melanoma Classification using metaheuristic algorithm

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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

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Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 5235-5283页

作者： Abdollahzadeh, Benyamin Khodadadi, Nima Barshandeh, Saeid Trojovsky, Pavel Gharehchopogh, Farhad Soleimanian El-kenawy, El-Sayed M. Abualigah, Laith Mirjalili, Seyedali Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA Afagh Higher Educ Inst Sch Engn Dept Comp Sci Orumiyeh Iran Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Univ Tabuk Comp Sci Dept Dept Biol Tabuk 47913 Saudi Arabia Al al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Obuda Univ Res & Innovat Ctr H-1034 Budapest Hungary

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm's performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the problem. To evaluate the proposed algorithm, 23 standard functions and CEC2019 functions were used and compared with different types of optimization algorithms. Moreover, using the statistical test T-test and the execution time to solve the problem have been discussed. Finally, it has been tested using four machine learning and data mining problems, and the results obtained from all the analysis signifies the excellent performance of this algorithm against all kinds of problems compared to other optimizers. This algorithm has performed better than the compared algorithms in 27 benchmarks out of 33 benchmarks and has obtained better results in solving the clustering problem in 7 data sets out of 10 data sets. Furthermore, the results obtained in the problems of community detection and feature selection and MLP were superior. The source codes of the PO algorithm are publicly available at https://***/matlabcentral/fileexchange/157231-puma-optimizer-po.

关键词： Optimization metaheuristic algorithm Puma optimization algorithm Machine learning Global optimization Automatic phase change

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

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

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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

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ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

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SOFT COMPUTING 2023年第18期27卷 13553-13581页

作者： Mostafa, Reham R. El-Attar, Noha E. Sabbeh, Sahar F. Vidyarthi, Ankit Hashim, Fatma A. Mansoura Univ Informat Syst Dept Fac Comp & Informat Sci Mansoura 35516 Egypt Benha Univ Fac Comp & Artificial Intelligence Banha Egypt Univ Jeddah Coll Comp Sci & Engn Jeddah Saudi Arabia Jaypee Inst Informat Technol Dept CSE & IT Noida India Helwan Univ Fac Engn Helwan Egypt

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration-exploitation stages during the search process;moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the p

关键词： Sooty tern optimization Ant lion optimization Feature optimization metaheuristic algorithm High dimensional search space

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Improved metaheuristic algorithm Based Finite Element Model Updating of a Hybrid Girder Cable-Stayed Railway Bridge

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BUILDINGS 2022年第7期12卷 958-958页

作者： Qin, Shiqiang Yuan, Yonggang Gan, Yaowei Wang, Qiuping Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Peoples R China

This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm's accuracy and local convergence in finite element (FE) model updating. Based on the idea of "survival of the fittest" in biological evolution, the improvement strategy introduces random crossover and mutation operators into metaheuristic algorithms to improve the accuracy and stability of the solution. The effectiveness of the improvement strategy with three typical metaheuristic algorithms was comprehensively tested by benchmark functions and numerical simulations of a space truss structure. Meanwhile, the initial FE model of a railway hybrid girder cable-stayed bridge was updated to examine the effect of the improved metaheuristic algorithm within the FE model, updating for complex engineering structures. The results show that the accuracy and stability of the improved metaheuristic algorithm were improved by this process. After the initial FE model of the hybrid girder cable-stayed bridge was updated, the calculated frequencies and displacements were closer to the measured values, better representing the actual structure, and showing that this process can be used for baseline FE models of bridges.

关键词： metaheuristic algorithm model updating random crossover hybrid girder cable-stayed bridge kriging model

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