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Quadrotor Robust Fractional-Order Control Based on a Recent Bonobo optimization Algorithm

JOURNAL OF MATHEMATICS 2025年第1期2025卷

作者： Ziani, Faycal Benhammou, Aissa Hartani, Mohamed Amine Rezk, Hegazy Tahri Mohamed Univ Fac Technol Dept Elect Engn Smart Grids & Renewable Energies Lab Bechar Algeria Ahmed Draia Univ Fac Technol Dept Elect Engn Sustainable Dev & Comp Sci Lab Adrar Algeria Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia

In recent years, quadrotors have emerged as essential roles in robotics, and their diversity and usefulness emphasize their importance. This research work presents an in-depth analysis of a quadcopter in terms of modeling, control, and optimization, where central to the operation of quadcopters and all robotics systems is the idea of stability response. This paper discusses the possibility of providing quadcopter stability by demonstrating the impact of the fractional controller in sensitive systems. The five fractional parameters for each engine are also improved using the Bonobo optimization (BO) algorithm. The optimized results in this paper are compared with the algorithms used, such as Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Grey Wolf optimization (GWO). The fractional-order proportional integral derivative (FOPID) controller has greater control power compared to its classic counterpart, PID control, as it provided improvement in minimizing overshoot by 90%, and it showed great improvement in settling and rising times using GWO 25% and BO 50% with some superiority of BO. By examining both the advantages and constraints inherent in these methodologies, we seek to advance the field forward, promoting more breakthroughs in this crucial area.

关键词： fractional-order control optimization algorithms quadrotor

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A Novel Manifold optimization Algorithm With the Dual Function and a Fuzzy Valuation Step

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1617-1626页

作者： Liu, Weiping Liu, Youfa Li, He Zou, Jingui Wuhan University School of Computer Science Wuhan430072 China Wuhan University School of Geodesy and Geomatics Wuhan430072 China

Fuzzy mathematical theory is widely used, fuzzy optimization is a branch of fuzzy mathematical theory, the significant application area is artificial intelligence in computer science, especially machine learning (deep learning) and pattern recognition. Fuzzy mathematics, especially fuzzy optimization, has become a bridge between the manifold optimization theory and deep learning applications, which is an essential theoretical foundation. The manifold optimization algorithm employs the projection method, which is unstable. In order to resolve the problem, in this article, the theory and methodology of manifold optimization concerning real and complex spaces is fully considered. Our primary focus is on the Riemannian manifold, where a groundbreaking optimization algorithm with the dual function and a fuzzy valuation step is proposed. To accelerate the convergence and enhance the stability of the optimization algorithm, a novel learning rate is present, which is referred as bivariate gradual learning rate warm-up. A comprehensive analysis of its convergence rates is conducted in various scenarios and the experiments results substantiate our discoveries, and demonstrate the correctness and effectiveness of our devised algorithm. © 1993-2012 IEEE.

关键词： optimization algorithms

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MOSSA: An Efficient Swarm Intelligent Algorithm to Solve Global optimization and Carbon Fiber Drawing Process Problems

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IEEE Internet of Things Journal 2025年第9期12卷 11940-11953页

作者： Xue, Jiankai Zhang, Chenglong Wang, Muming Dong, Xuezhe Anhui University of Technology School of Computer Science and Technology Ma’anshan243032 China Chinese University of Hong Kong School of Data Science Shenzhen518172 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China University of Calgary Department of Chemical and Petroleum Engineering CalgaryABT2N 1N4 Canada Hong Kong Polytechnic University Department of Applied Physics Hong Kong

In this article, the sparrow search algorithm (SSA) is extended to the multiobjective SSA (MOSSA) with the purpose of efficiently solving the multiobjective optimization problems (MOPs). First, the MOSSA adaptively evaluates nondominated sparrow individuals stored in the external archive (EA) by using an adaptive mesh approach, which is utilized to obtain the best producer. Second, the scrounger sparrows adjust their trajectories according to the location of the best producer, called the scrounger follow strategy, which can improve the quality of the solutions when solving MOPs. Then, the proposed scouter search strategy is capable of maintaining population diversity and accelerate convergence. Moreover, the EA is pruned with the aim of avoiding the waste of computing resources. Extensive experiments with 22 benchmark examples validate the effectiveness of our approach against six state-of-the-art optimization approaches. Finally, the MOSSA is applied in the carbon fiber drawing process problems and the stretching parameters obtained by the MOSSA is reasonable. © 2014 IEEE.

关键词： optimization algorithms

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Distributed asynchronous double accelerated optimization for ethylene plant considering delays

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Chinese Journal of Chemical Engineering 2025年 78卷 245-250页

作者： Wang, Ting Li, Zhongmei Du, Wenli State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm. © 2024 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press Co., Ltd. (CIP)

关键词： optimization algorithms

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Accelerated Dynamic Event-Triggered Algorithm for Online Power System optimization by Using Powerball Technique

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IEEE Transactions on Smart Grid 2025年第3期16卷 2348-2360页

作者： Hu, Chuanhao Zhang, Xuan Wu, Qiuwei Central South University School of Transportation Changsha410075 China Tsinghua University Smart Grid and Renewable Energy Laboratory Tsinghua Shenzhen International Graduate School Shenzhen518055 China Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT8 5BN United Kingdom

Nowadays, online optimization for power systems has gained increasing attention due to many time-varying scenarios in practical applications. This paper proposes a novel feedback-based online algorithm for power system optimization problems by combining the powerball technique and a dynamic event-triggered mechanism, aiming to achieving a faster convergence rate and high communication efficiency simultaneously. In particular, a measurement-based prima-dual approach is utilized to solve the considered time-varying optimization problem. Taking the constrained resource issue, a more flexible triggering condition with a dynamic threshold is designed for dual variables to reduce communication burden. By further employing the powerball strategy, the primal-dual iteration is modified with an improved convergence rate. Finally, numerical experiments on test systems are conducted to demonstrate the effectiveness of the proposed online algorithm. It is shown that the proposed approach has superior convergence property especially in the initial process. Meanwhile, a better trade-off between the satisfactory system performance and communication resources consumption can be achieved. © 2010-2012 IEEE.

关键词： optimization algorithms

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EXISTENCE CONDITIONS AND RADIANT PROPERTIES OF SOLUTIONS TO UNCERTAIN VECTOR optimization PROBLEMS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and optimization 2025年第1期7卷 57-70页

作者： Duoc, Pham Thanh Le, Huy Minh Thu, Pham Tran Anh Faculty of Information Technology Can Tho University of Technology Cantho Viet Nam Faculty of Fundamental Sciences Van Lang University Ho Chi Minh City Viet Nam Department of Mathematics FPT University Cantho Viet Nam

In this paper, we consider nonconvex uncertain vector optimization problems, and discuss the properties of their robust efficient solution sets. First, existence conditions of the robust efficient solutions under consideration in both cases of compactness and noncompactness are addressed. Second, various types of generalized radiant properties for vector-valued mappings and their relationships are considered. Finally, the radiant property of the robust efficient solution sets to such problems is studied. ©2025 Applied Set-Valued Analysis and optimization.

关键词： optimization algorithms

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Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases

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SCIENTIFIC REPORTS 2025年第1期15卷 1-33页

作者： Malik, Saleem Patro, S. Gopal Krishna Mahanty, Chandrakanta Kumar, Saravanapriya Lasisi, Ayodele Naveed, Quadri Noorulhasan Kulkarni, Anjanabhargavi Buradi, Abdulrajak Emma, Addisu Frinjo Kraiem, Naoufel P A Coll Engn Dept Comp Sci & Engn Mangalore Karnataka India Sreenidhi Univ Sch Engn Hyderabad 501301 Telangana India GITAM Deemed Be Univ GITAM Sch Technol Dept Comp Sci & Engn Visakhapatnam 530045 India Sacred Heart Coll Autonomous Dept MCA Tirupattur 635601 Tamil Nadu India King Khalid Univ Coll Comp Sci Dept Comp Sci Abha Saudi Arabia Visvesvaraya Technol Univ Dept Comp Sci & Engn Belagavi India Nitte Meenakshi Inst Technol Bangalore India Dilla Univ Coll Engn & Technol Sch Mech & Automot Engn South Ethiopia Reg StatePOB 419 Dilla Ethiopia

In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). The COVID-19 pandemic has further emphasized the need for effective tools to predict and treat NCDs, especially in individuals at risk. This research addresses these pressing concerns by proposing a comprehensive framework that combines advanced data mining techniques, feature selection, and meta-heuristic optimization. The proposed framework introduces novel hybrid algorithms, including the Hierarchical Genetic Multiple Reduct Selection Algorithm (H-GMRA) and the Customized Function-based Particle Swarm optimization with Rough Set Theory for NCD Feature Selection (CPSO-RST-NFS). These algorithms aim to address the challenges of feature selection, computational complexity, and disease classification accuracy. H-GMRA outperforms traditional methods by identifying minimal feature sets with high dependency ratios. CPSO-RST-NFS combines meta-heuristic optimization with feature selection, resulting in improved efficiency and accuracy. Through extensive experimentation on diverse NCD datasets, this research demonstrates the framework's ability to select informative features, improve classification accuracy, and contribute to better patient outcomes. By bridging the gap between computational efficiency and disease classification accuracy, this work offers valuable insights for healthcare practitioners and data analysts, ultimately advancing the field of NCD research. The proposed framework presents a significant step towards enhancing the early detection and management of NCDs, offering hope for more precise clinical predictions and improved patient care.

关键词： Non-Communicable diseases Feature selection optimization algorithms

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Constrained Multi-Objective optimization Algorithm Based on the Boundary Value Reservation Strategy

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IAENG International Journal of Applied Mathematics 2025年第2期55卷 297-306页

作者： Xuan, Hejun Zhou, Deming Ding, Yan Ma, Ruiyan Zhu, Canyu Feng, Yan School of Computer and Information Technology Xinyang Normal University Henan Xinyang46400 China

Constrained multi-objective optimization problems are ubiquitous in real life. However, the presence of constraints makes the feasible domain complex, discontinuous and narrow. Consequently, solving multi-objective optimization problems becomes exceptionally challenging. Existing methods struggle to efficiently find the Pareto front (PF) with both good convergence and uniformity. To address this issue effectively, this paper proposed a two-stage constrained multi-objective optimization algorithm based on a three-population evolutionary algorithm (TPEA). In the first stage, the TPEA algorithm is employed to search for approximately constrained Pareto front (CPF). In the second stage, a boundary value retention mechanism was designed to enhance the quality of CPF. Additionally, a local search method with an adaptive perturbation factor is introduced to generate offspring, thereby increasing the probability of generating a boundary point. To select the better individuals for the next generation, a forward-looking environmental selection strategy was proposed. The proposed algorithm is compared with five algorithms. In the experiments, 38 benchmark test functions and three real-world problems are employed. Experimental results demonstrate that the proposed algorithm can obtain the CPFs with better convergence, uniformity, and diversity than five compared algorithms. © (2024), (International Association of Engineers). All rights reserved.

关键词： optimization algorithms

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ON THE CONVERGENCE OF optimization PROBLEMS WITH KERNEL DENSITY ESTIMATED PROBABILISTIC CONSTRAINTS

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and optimization 2025年第2期7卷 209-221页

作者： Schuster, Michael Department of Mathematics Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

Uncertainty plays a significant role in applied mathematics and probabilistic constraints are widely used to model uncertainty in various fields even if probabilistic constraints often demand computational challenges. Kernel density estimation (KDE) provides a data-driven approach for properly estimating probability density functions and efficiently evaluating corresponding probabilities. In this paper, we investigate optimization problems with probabilistic constraints, where the probabilities are approximated using a KDE approach. We establish sufficient conditions under which the solution of the KDE approximated optimization problem converges to the solution of the original problem as the sample size goes to infinity. The main results of this paper include three theorems: (1) For sufficiently large sample sizes, the solution of the original problem is also a solution of the approximated problem, if the probabilistic constraint is passive;(2) The limit of a convergent sequence of solutions of the approximated problems is a solution of the original problem, if the KDE uniformly converges;(3) We provide sufficient conditions for the existence of a convergent sequence of solutions of the approximated problems. © 2025 Applied Set-Valued Analysis and optimization.

关键词： optimization algorithms

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Improving Shallow Foundation Settlement Prediction through Intelligent optimization Techniques

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 747-766页

作者： Hadi Fattahi Hossein Ghaedi Danial Jahed Armaghani Faculty of Earth Sciences Engineering Arak University of TechnologyArak38135-1177Iran School of Civil and Environmental Engineering University of Technology SydneySydneyNSW 2007Australia

In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes *** is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil ***,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s *** addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock *** study explores the application of innovative intelligent techniques to predict S_(m) to address these ***,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this *** modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data ***,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data *** findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronou

关键词： Shallow foundations optimization algorithms settlement prediction intelligent methods sensitivity analysis

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