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Journal of Engineering Research (Kuwait) 2024年

作者： El-Shorbagy, M.A. Elazeem, A.M. Abd Department of Mathematics College of Science and Humanities in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Basic Engineering Science Faculty of Engineering Menoufia University Shebin El-Kom 32511 Egypt Basic Sciences Department Osim Higher Institute of Administrative Sciences Giza Egypt Faculty of Business Administration Horus University New Damietta Egypt

In this paper, a novel metaheuristic optimization algorithm (MHOA) called convex combination search (CCS) is proposed as a solution to global optimization problems and engineering design problems. CCS is based on a combination of rules that depend upon the concept of the linear convex combination. These rules are mathematically modeled to guarantee the variety of solutions at the initialization stage and achieve equilibrium between exploitation, exploration capabilities at the generation stage, the algorithm's convergence, and robustness. A detailed mathematical model of the algorithm is offered. As an advantage for the CCS algorithm, it requires just two parameters which are the population size and the number of generations for determining the global optimal solution of any optimization problem. The effectiveness of the suggested algorithm is investigated on 17 unconstrained multimodal test functions, and 7 constrained benchmark problems having different characteristics. In addition, five engineering design challenges are resolved to confirm the robustness and dependability of CCS in resolving engineering applications. The efficiency and competitiveness of the proposed algorithm were illustrated in comparison with other methods. A statistical analysis of the results has been carried out to illustrate the competitiveness and power effectiveness of the proposed algorithm. Finally, the sensitivity of the CCS parameters is presented to show the sensitivities of these parameters to the performance of the proposed algorithm. © 2024 The Authors

关键词： Convex combination search Engineering design problems Global optimization problems metaheuristic algorithm Optimization

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Tuning of cascade PID controller gains of quadcopter under bounded disturbances using metaheuristic based research algorithm

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AERONAUTICAL JOURNAL 2025年

作者： Copur, E. H. Balta, E. Bilgic, H. H. Necmettin Erbakan Univ Dept Aerosp Engn TR-42040 Konya Turkiye Necmettin Erbakan Univ Dept Aeronaut Engn TR-42040 Konya Turkiye

The proportional-integral-derivative (PID) controller remains widely used in industrial applications today due to its simplicity and ease of implementation. However, tuning the controller's gains is crucial for achieving desired performance. This study compares the performance of PID controllers within a cascade control architecture designed for both position and attitude control of a quadcopter. Particle swarm optimisation (PSO), grey wolf optimisation (GWO), artificial bee colony (ABC), and differential evaluation (DE) methods are employed to optimally tune the PID parameters. A set of PID gains is determined offline by minimising various modified multi-objective functions based on different fitness measures: IAE, ISE, ITAE and ITSE. These measures are adapted as fitness functions for position and attitude control. A simulation study is conducted to determine which fitness function yields the optimal PID gains, as evidenced by the lowest values of the objective functions. In these simulations, two different desired trajectories are designed, and the controllers are applied to ensure the quadcopter tracks these trajectories accurately. Additionally, to test the robustness of the flight control architecture and the finely tuned PID controllers against various environmental effects and parametric uncertainties, several case scenarios are also explored.

关键词： Cascade PID controller search algorithm metaheuristic algorithm quadcopter optimization

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Three stage based reinforcement learning for combining multiple metaheuristic algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Liu, Xiaotong Wang, Tianlei Zeng, Zhiqiang Tian, Ye Tong, Jun Wuyi Univ Sch Mech & Automat Engn Jiangmen 529020 Peoples R China Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2500 Australia Jiangmen Key Lab Kejie Semicond Bonding Technol & Jiangmen 529020 Peoples R China

Combined of metaheuristic algorithms can effectively improve the performance of algorithms by utilizing the characteristics of different metaheuristic algorithms, and the key is how to combine multiple metaheuristic algorithms. Reinforcement learning is one of the effective methods for combining multiple metaheuristic algorithms. However, designing a competitive reinforcement learning approach to achieve efficient collaboration among metaheuristic algorithms is a highly challenging task. Therefore, this study proposes a three-stage reinforcement learning for combining multiple metaheuristic algorithms (TSRL-CMM). TSRL-CMM is divided into three stages: the exploration stage, the stage with both exploration and exploitation, and the exploitation stage. On this basis, an adaptive action selection strategy and a reward function are designed. The proposed action selection strategy can adaptively select appropriate metaheuristic algorithms based on the state of the population, achieving a balance between exploration and exploitation. The proposed reward function can effectively guide the population to transition to the expected state based on the iteration stage and state transitions. To verify the effectiveness of TSRL-CMM, we evaluated it using the CEC2017 test suite, nine real-world engineering design problems and six power system optimization problems. TSRL-CMM was compared with 10 state-of-the-art metaheuristic algorithms, and experimental results showed that TSRL-CMM performed better than the compared algorithms in both artificial and real-world problems. Furthermore, TSRL-CMM was specifically compared with three CEC winner algorithms on the CEC 2017 benchmark test suite. The experimental results show that the proposed algorithm is highly competitive. The source code can be obtained from https://***/xtongliu/TSRL-CMM-code.

关键词： Reinforcement learning Combination algorithm metaheuristic algorithm Engineering optimization

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Dynamic synergy optimization (DSO): An integrated approach of metaheuristic algorithms and PID control for real-time stability enhancement in refrigeration systems

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INTERNATIONAL JOURNAL OF REFRIGERATION 2025年 173卷 100-110页

作者： Li, Jieyu Xie, Xingxiang Dai, Leyang Xu, Lijie Jimei Univ Inst Marine Engn Prov Key Lab Naval Architecture Ocean Engn Xiamen 361021 Peoples R China

With increasing global energy demand, improving the efficiency of refrigeration systems and reducing their environmental impact is crucial, especially since refrigeration often accounts for a significant portion of energy consumption. Traditional Proportional-Integral-Derivative (PID) control methods struggle with the complex, nonlinear nature of refrigeration systems, resulting in slow response times and limited optimization capabilities. While metaheuristic algorithms can perform global searches, they often lack the real-time fine-tuning necessary for optimal dynamic control. This study introduces Dynamic Synergy Optimization (DSO), a novel framework that integrates metaheuristic algorithms with PID control. Unlike conventional methods that only turn PID parameters using metaheuristics, DSO combines the global optimization power of metaheuristics with the real-time adjustment capabilities of PID, providing effective global search and local refinement. The PID controller ensures quick adaptation and system stability. Experimental results show that the Harris Hawks Optimization algorithm integrated with PID control outperforms standard PID control with a 63.3 % reduction in response time, a 69.2 % decrease in stabilization time, and a 19.65 % enhancement in energy efficiency. The DSO strategy significantly enhances the dynamic response and stability of refrigeration systems, reduces hysteresis, and accelerates the attainment of steady-state operation.

关键词： Dynamic synergy optimization (DSO) metaheuristic algorithm PID control Energy efficiency ratio Refrigeration systems

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Application of metaheuristic algorithms with Supervised Machine Learning for Accurate Power Consumption Prediction

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COGNITIVE COMPUTATION 2025年第1期17卷 1-35页

作者： Wang, Mengxia Zhu, Chaoyang Zhang, Yunxiang Deng, Jinxin Cai, Yiwei Wei, Wei Guo, Mengxing Commun Univ China Inst Social Innovat & Publ Culture Beijing 100000 Peoples R China Int Engn Psychol Inst US Denver CO 80201 USA Univ Illinois Champaign IL 61820 USA Hainan Vocat Univ Sci & Technol Haikou 570100 Peoples R China Shenzhen High Level Talents Dev Promot Assoc Shenzhen 518000 Peoples R China CDA Int Accelerator Shenzhen 518000 Peoples R China Beijing Inst Technol Shenzhen Res Inst Shenzhen 518000 Peoples R China Univ Wollongong Wollongong City 2223 Australia Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shandong Open Univ Jinan 250000 Peoples R China

Accurate power consumption prediction is a crucial part of energy management. Some of the machine learning models that are the focus of this study for the prediction of power use include Support Vector Regression, Adaptive Boosting, and Decision Tree Regression. These models have been improved with the use of some novel optimizers-namely, the Trochoid Search Optimization, Red-Tailed Hawk, and Giant Armadillo Optimization methods-for hyper-parameter tuning to enhance prediction accuracy. When tested against real data, DTGA outperformed with R2 values of 0.9918, 0.9924, and 0.9934 for three zones. This work extends the study on the forecast of power consumption by integrating machine learning and optimization techniques that provide effective energy management strategies.

关键词： Power consumption prediction Optimization techniques Support vector regression metaheuristic algorithm Machine learning

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Gold Seekers algorithm: An Innovative metaheuristic Approach for Global Optimization and Its Application in Image Segmentation

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第9期38卷 2114-2129页

作者： Ehsaeyan, E. Sirjan Univ Technol Elect Engn Dept Sirjan Iran

A significant disadvantage of meta-heuristic algorithms is their tendency to get stuck in local optima, which prevents them from finding the global optimal solution and potentially limits their overall effectiveness. To mitigate this drawback, a novel metaheuristic optimization algorithm known as the Gold Seekers algorithm (GSA) is introduced in this paper to address complex optimization problems. This algorithm mimics the competitive behavior seen among miners hunting for gold. GSA has several key advantages, including fast convergence due to parallel execution, low sensitivity to control parameters due to adaptive coefficients, and high accuracy in global searches thanks to its structured division of search agents into specific roles. The exploitation phase involves searching near the leader to identify valuable areas, while the exploration phase focuses on searching among gold seekers to discover unknown golden locations. To validate GSA's effectiveness, the algorithm was tested on 20 classical benchmark functions of various types and dimensions (30, 60, and 90) and applied to image segmentation to find optimal thresholds. Performance in this practical application was compared to eight well-known heuristic techniques from existing *** findings indicate that the suggested algorithm achieves superior solution accuracy and expedited convergence across most benchmark functions, in addition to determining optimal thresholds for image segmentation tasks.

关键词： Gold Seekers algorithm metaheuristic algorithm Swarm Intelligence Optimization Techniques Benchmark Problems Image Thresholding

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An efficient prediction of punching shear strength in reinforced concrete slabs through boosting methods and metaheuristic algorithms

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STRUCTURES 2025年 74卷

作者： Khajavi, Erfan Khanghah, Amir Reza Taghavi Khiavi, Ali Javadzade Islamic Azad Univ Dept Civil Engn Ardebil Iran Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran

Safety and optimum designs in civil engineering applications depend on correctly estimating critical parameters such as punching shear strength (Pu) in concrete slabs. This work, therefore, studies improvement in prediction accuracy and generalization by applying five advanced boosting machine learning methods to overcome traditional empirical methods' drawbacks. Five state-of-the-art boosting models, namely, XGBoost Regression (XGBr), Histogram Gradient Boosting Regression (HGBr), Light Gradient Boosting Regression (LGBr), CatBoost, and AdaBoost, were conducted in modeling Pu prediction. K-fold cross-validation has been conducted to select only the best two models for further elaboration with state-of-the-art optimization algorithms, including Quadratic Interpolation Optimization (QIO), Red-tailed Hawk algorithm (RTH), Giant Armadillo Optimization (GAO), and Greylag Goose Optimization (GGO). Some of the ensemble techniques were also used to enhance robustness and accuracy. Several sensitivity analysis methods were conducted to determine the effect of input parameters on model performance. Indeed, very important insight was provided about major contributing factors. The proposed techniques have certain significant advantages over others. It can process nonlinear relationships, provide a minimum prediction error, and recognize those parameters critical to punching shear. This paper emphasizes how integrating machine learning with optimization and sensitivity analysis can enable the development of stateof-the-art predictive modeling in structural engineering that is reliable and more efficient than traditional methods.

关键词： Punching shear strength Concrete slab Boosting models metaheuristic algorithm Ensemble method

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Prediction of venous clinical severity score in yoga practitioners and non-practitioners using discriminant analysis and metaheuristic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Wang, Fengcai Wang, Fei Jimei Univ Chengyi Coll Dept Phys Educ & Arts Xiamen 361021 Fujian Peoples R China Minist Sports Xiamen Inst Technol Xiamen 361021 Fujian Peoples R China

Venous Clinical Severity Score is a key measure of the severity of venous disease. A new prediction method VCSS for yogic practitioners and non-practitioners using both theoretical knowledge and advanced computational techniques has been presented in this study. Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Robust Sparse Linear Discriminant Analysis were used as classifiers, enhanced by feature selection methods to identify the most critical predictors that improve the mathematical and theoretical interpretability the dataset. In an attempt to enhance model performance, some new hybridization methods were applied: the integration of the Tyrannosaurus Optimization algorithm, Brown Bear Optimization, and Walrus Optimization algorithm. The modeling process, designed with k-fold cross-validation, was carried out carefully to avoid biases and provide robust and reliable predictions. The novelty of this dataset, involving people with and without yoga practice, brings about a newer perspective in understanding the impacts of lifestyles on venous health. These results prove that the hybrid Linear Discriminant Analysis-Brown Bear Optimization model outperforms the others in terms of accuracy, justifying optimization algorithms' role in modern predictive analytics. The contribution of the present study does not stop at underlining the health relevance of yoga but also showcases how feature selection and optimization techniques go hand-in-hand in fine-tuning discriminant models, contributing significantly to health research and predictive modeling.

关键词： Yoga practice Venous Clinical Severity Score Linear Discriminant Analysis Quadratic Discriminant Analysis metaheuristic algorithm

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Structural bias in metaheuristic algorithms: Insights, open problems, and future prospects

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Rajwar, Kanchan Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Indian Stat Inst Stat Qual Control & Operat Res Unit Hyderabad 500007 Telangana India

This paper addresses a critical issue of structural bias in metaheuristic algorithms, a key factor that often hinders their effectiveness in solving complex optimization problems. Such biases, typically resulting from the design of algorithmic operators and solution construction processes, can lead to a decrease in performance over time. Despite its importance, structural bias is little understood and rarely explored. Moreover, the theoretical framework for structural bias in this context is notably underdeveloped. To the best of our knowledge, no comprehensive review of structural bias in metaheuristic algorithms is available to date. Consequently, this study is subjected to a thorough literature review, providing the mathematical definition of structural bias, the theoretical background, and an extensive analysis of its various forms within metaheuristic algorithms. This paper discusses structural bias in several metaheuristic algorithms, including the Genetic algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization. Methodologies for identifying structural bias, currently scattered across several studies, are categorized into four classes and discussed through the implementation of Particle Swarm Optimization, highlighting their advantages and limitations. Additionally, five critical open problems are identified, and essential research directions for future exploration are outlined. As the first comprehensive review of structural bias - an issue gaining increasing attention - this work is expected to serve as a vital resource for algorithm designers and the research community.

关键词： metaheuristic algorithm Evolutionary algorithm Structural bias Optimization technique

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A Systematic Review of metaheuristic algorithms in Human Activity Recognition: Applications, Trends, and Challenges

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 731-742页

作者： Kisoi, John Deutero Yusup, Norfadzlan Junaini, Syahrul Nizam Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan Malaysia

metaheuristic algorithms have emerged as promising techniques for optimizing human activity recognition (HAR) systems. This systematic review examines the application of these algorithms in HAR by analyzing relevant literature published between 2019 and 2024. A comprehensive search across multiple databases yielded 27 studies that met the inclusion criteria. The analysis revealed that Genetic algorithms (GA) exhibit classification accuracy rates ranging from 88.25% to 96.00% in activity recognition and up to 90.63% in localization tasks. Notably, Oppositional and Chaos Particle Swarm Optimization (OCPSO) combined with MI-1DCNN significantly improves detection accuracy, demonstrating a 2.82% improvement over standard PSO with Support Vector Machine (SVM) as classifier approaches. Our analysis highlights a growing trend toward hybrid metaheuristic approaches that enhance feature selection and classifier optimization. However, challenges related to computational cost and scalability persist, underscoring key areas for future research. These findings emphasize the potential of metaheuristic algorithms to significantly advance HAR. Future studies should explore the development of more computationally efficient hybrid models and the integration of metaheuristic optimization with deep learning architectures to enhance system robustness and adaptability.

关键词： metaheuristic algorithm human activity recognition systematic review application trend challenge literature

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