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A comparative performance of different Type-1 tournament based metaheuristic algorithms in solving engineering beam design optimization problems and structural engineering design problems

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Soft Computing 2025年 1-28页

作者： Mandal, Goutam Kumar, Nirmal Bhunia, Asoke Kumar Department of Mathematics The University of Burdwan Golapbag W.B Purba Burdwan 713104 India Department of Mathematics Brainware University 398 Ramkrishnapur Road Jagadighata Market Barasat West Bengal Kolkata 700125 India Department of Science and Humanities Purulia Polytechnic West Bengal Purulia 723147 India

Tournamenting process is one of the effective approaches for formulating hybrid algorithm in the recent years. In this work, four hybrid algorithms through tournamenting process are developed with the help of four popular and well-known metaheuristic algorithms, viz. teaching learning based optimization (TLBO), embedded cuckoo search (ECS), social group optimization (SGO) and honey badger algorithm (HBA). The resulting algorithms are named as T1-TLBO, T1-ECS, T1-SGO and T1-HBA. These algorithms are applied to solve structural engineering design problems. The results executed by proposed algorithms are compared numerically with a number of recently developed metaheuristic algorithms. Finally, to analyse the statistical significance of the proposed hybrid algorithms, two nonparametric statistical tests are performed and a fruitful conclusion is drawn. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： ECS Engineering design problems HBA Hybrid algorithm metaheuristic algorithm SGO TLBO

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metaheuristic-Driven Abnormal Traffic Detection Model for SDN Based on Improved Tyrannosaurus Optimization algorithm

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Computers, Materials and Continua 2025年第3期83卷 4495-4513页

作者： Hui Xu Jiahui Chen Zhonghao Hu School of Computer Science Hubei University of Technology Wuhan 430068 China

Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the metaheuristic algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in local optimums, and has poor search accuracy. Then, this paper tries to improve TROA, namely the Improved Tyrannosaurus Optimization algorithm (ITROA). It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA. Finally, the validity of the ITROA is verified by the benchmark function and the UCI dataset, and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection. The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN, achieving an accuracy of 99.37% on binary classification and 96.73% on multiclassification.

关键词： Software-defined networking abnormal traffic detection feature selection metaheuristic algorithm tyrannosaurus optimization algorithm

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Optimizing semiconductor process recipe settings using hybrid meta-learning and metaheuristic approaches

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INFORMATION SCIENCES 2025年 706卷

作者： Chen, Zhen-Yin Annie Lin, Chun-Cheng Lu, Ke-Wen Natl Yang Ming Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan Natl Chin Yi Univ Technol Int Master Program Smart Mfg & Appl Informat Sci Taichung 411 Taiwan

Achieving high-yield production in semiconductor thin film chemical vapor deposition (CVD) processes requires precise parameter settings, which often rely on costly R&D efforts and expert experience. This work proposes a novel approach combining meta-learning with metaheuristic algorithms to optimize these parameters more efficiently, particularly when experimental data is limited. Collaborated with a semiconductor manufacturer specializing in low-volume, high-variety products, we addressed the challenge of limited experimental data. To optimize new product processes, the company collected limited real experiment data. However, conventional soft computing methods often require extensive data for accurate prediction models, increasing computational time and costs. Therefore, we propose hybrid meta-learning and metaheuristic approaches to efficiently determine parameter settings. Meta-learning leverages historical data from similar tasks to train a neural acquisition function within the meta Bayesian optimization (MetaBO) framework. Two enhancements are proposed: employing the Halton sequence to reduce computational complexity and integrating three metaheuristic algorithms (genetic algorithm, particle swarm optimization PSO, and artificial fish school algorithm) to refine evaluation points and improve model quality. Experiments on benchmarking black-box functions and real semiconductor CVD processes show superior performance of our approaches over legacy MetaBO and other acquisition function methods, with the PSO-incorporated hybrid approach performing best.

关键词： Meta-learning Transfer learning Neural acquisition function Halton sequence metaheuristic algorithm Smart semiconductor manufacturing

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Integrated Runoff Forecasting Model with Mode Decomposition and metaheuristic-optimized Bidirectional Gated Recurrent Unit

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WATER RESOURCES MANAGEMENT 2025年第6期39卷 2763-2784页

作者： Feng, Zhong-kai Liu, Wen-jie Tang, Zheng-yang Feng, Bao-fei Ji, Guo-liang Xu, Yin-shan Niu, Wen-jing Minist Water Resources Key Lab Water Secur Guarantee Guangdong Hong Kong Hong Kong 510610 Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Yangtze Power Co Ltd Dept Water Resources Management Yichang 443133 Peoples R China Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Three Gorges Corp River Basin Hub Adm Yichang 443100 Peoples R China

The effective prediction and simulation of nonstationary hydrological time series play a critical role in the rational allocation of limited water resources. To enhance forecasting accuracy and provide valuable technical support for dispatching operations, this study presents an integrated hydrological time series prediction model that combines mode decomposition, machine learning, and metaheuristic algorithms. First, the adaptive chirp mode decomposition is used to extract components of varying resolutions from the nonstationary hydrological time series. Next, a bidirectional gated recurrent unit is selected as the predictor to capture the complex relationships between inputs and outputs for each component, with computational parameters optimized using the marine predators algorithm. Finally, the predicted outcomes for all components are integrated to generate the final forecasting results. Real-world runoff data from multiple hydrological stations validate the effectiveness of the proposed model. Extensive experiments, evaluated using multiple metrics, demonstrate that the model consistently outperforms traditional approaches in a range of scenarios. Thus, a reliable machine learning tool is offered for accurate hydrological forecasting.

关键词： Nonstationary hydrological prediction metaheuristic algorithm Mode decomposition Artificial intelligence

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Scalable bearing fault diagnosis using metaheuristic feature selection and machine learning for diverse operating conditions

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2025年第1期13卷

作者： Nayana, B. R. Subha, R. Radhakrishnan, Rekha Geethanjali, P. MS Ramaiah Univ Appl Sci Dept Comp Sci Engn Bangalore India MS Ramaiah Inst Technol Dept Elect & Elect Engn Bangalore India Sir M Visvesvaraya Inst Technol Dept Elect & Elect Engn Bangalore India Vellore Univ Sch Elect Engn Vellore India

Bearings are the foremost component of machinery that fails;hence early diagnosing mechanisms aided by artificial intelligence techniques play a crucial role. The objective of this study is to develop a scalable model for real-time bearing fault diagnosis using recent evolutionary algorithms such as, Marine Predator algorithm, Equilibrium Optimization, Henry Gas Solubility Optimization, and Poor and Rich Optimization for optimal Feature Selection. Forty time-domain features were extracted from the bearing fault recordings from Case Western Reserve University, Machinery Fault Prevention Technology, Machinery Fault Dataset, and Paderborn University, with classes ranging from 3 to 48. The performance of the algorithms is investigated in terms of feature dimension, accuracy, and processing time. The performance of the five relevant feature groups that are identified is validated using four classical machine learning models and the most promising feature set that consistently performed well across all four datasets, regardless of the classifier model is identified. The compact feature set combined with the simplicity of the ML model delivers a computationally simple, yet effective feature extraction and classification model scalable for real-time bearing fault diagnosis under diverse operating conditions. The results obtained have been compared with existing works to demonstrate the resilience of the model.

关键词： Bearing fault diagnosis computational intelligence feature selection metaheuristic algorithm machine learning

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Enhanced Wind Power Forecasting Using a Hybrid Multi-Strategy Coati Optimization algorithm and Backpropagation Neural Network

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SENSORS 2025年第8期25卷 2438-2438页

作者： Yang, Hua Shu, Zhan Li, Zhonger Wuhan Polytech Univ Coll Math & Comp Sci Wuhan 430023 Peoples R China

The integration of intermittent wind power into modern grids necessitates highly accurate forecasting models to ensure stability and efficiency. To address the limitations of traditional backpropagation (BP) neural networks, such as slow convergence and susceptibility to local optima, this study proposes a novel hybrid framework: the Multi-Strategy Coati Optimization algorithm (SZCOA)-optimized BP neural network (SZCOA-BP). The SZCOA integrates three innovative strategies-a population position update mechanism for global exploration, an olfactory tracing strategy to evade local optima, and a soft frost search strategy for refined exploitation-to enhance the optimization efficiency and robustness of BP networks. Evaluated on the CEC2017 benchmark, the SZCOA outperformed state-of-the-art algorithms, including ICOA, DBO, and PSO, achieving superior convergence speed and solution accuracy. Applied to a real-world wind power dataset (912 samples from Alibaba Cloud Tianchi), the SZCOA-BP model attained an R-2 of 94.437% and reduced the MAE to 10.948, significantly surpassing the standard BP model (R-2: 81.167%, MAE: 18.891). Comparative analyses with COA-BP, BWO-BP, and other hybrid models further validated its dominance in prediction accuracy and stability. The proposed framework not only advances wind power forecasting but also offers a scalable solution for optimizing complex renewable energy systems, supporting global efforts toward sustainable energy transitions.

关键词： wind power prediction hybrid optimization model metaheuristic algorithm BP neural network renewable energy integration

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A point-interval prediction framework for minimum miscibility pressure of CO2-crude oil systems

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FUEL 2025年 381卷

作者： Chen, Haoyu Zheng, Yong Huang, Hai Wang, Zhigang Yang, Bing Ni, Jun Xian Shiyou Univ Coll Petr Engn Xian 710065 Peoples R China Minist Educ Engn Res Ctr Dev & Management Low Ultralow Permeab Xian 710065 Peoples R China Shaanxi Yanchang Petr Grp Co Ltd Xian 710075 Peoples R China

CO2 flooding is among the most widely employed techniques for enhanced oil recovery (EOR). Its efficiency is highly dependent on the minimum miscibility pressure (MMP) of the CO2-crude oil system, making accurate prediction essential. This work innovatively proposes a joint MMP point-interval prediction method based on multiple artificial intelligence techniques, considering prediction uncertainties. The combined model of the Convolutional Neural Network-Hybrid Kernel Least Squares Support Vector Machine (CNN-HKLSSVM) integrates the strengths of deep learning and traditional models, providing significant advantages in point prediction. The Adaptive Bandwidth Kernel Density Estimation (ABKDE) introduces a sensitivity factor for adaptive bandwidth changes, ensuring reliable interval predictions. The Improved Black-winged Kite algorithm (IBKA) also incorporates various improvement strategies to determine the model hyperparameters effectively. The experiments show that the Root Mean Square Error (RMSE) of the proposed model in point prediction is only 1.323, which is lower than all comparison models. The prediction accuracy surpasses that of the six existing prediction models. Additionally, the comprehensive evaluation index F within a 95% confidence interval in interval prediction is 1.91, significantly better than other methods. The experimental results show that proposed approach demonstrates strong point and interval prediction performance, effectively improving oil recovery.

关键词： CCUS Minimum miscibility pressure Combined model Interval prediction metaheuristic algorithm

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metaheuristic algorithms for Multiagent Routing Problems

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AUTOMATION AND REMOTE CONTROL 2021年第10期82卷 1787-1801页

作者： Germanchuk, M. S. Lemtyuzhnikova, D., V Lukianenko, V. A. Vernadsky Crimean Fed Univ Simferopol 295007 Russia Russian Acad Sci Trapeznikov Inst Control Sci Moscow 117997 Russia Moscow Inst Aviat Technol Moscow 125993 Russia

The problems of constructing routes in complex networks by many sales agents are considered. Formalization leads to pseudo-Boolean discrete optimization problems with constraints that take into account the specifics of route construction. The sparsity of the constraint matrix permits one to apply decomposition approaches and network clustering. The development of approximate algorithms for selecting routes in complex networks involves taking into account the properties of the network structure, its complexity, the presence of restrictions, regulations, reachability conditions, and the number of sales agents. It is shown that the solution of routing problems can be based on the application of a multiagent approach in combination with clustering (decomposition) of the original problem and metaheuristics. Multiagent systems with swarm intelligence are used to solve complex discrete optimization problems that cannot efficiently be solved by classical algorithms. The agent model for a complex network of a problem of the many traveling salesmen type becomes an intelligent system that defines heuristic algorithms for finding the optimal solution by reactive agents (that follow the rules laid down in them). We use and describe in detail the composition of the algorithms, which have proven themselves well in computational experiments;those are a modification of the genetic algorithm, ant colony optimization, artificial bee colony algorithm, and simulated annealing. A generalized algorithm is proposed and implemented in which a simpler network (a flyover network) is matched to the source network. Here a numerical experiment was carried out for the problem of routing on a GIS map for urban infrastructure. Clustering algorithms are implemented in which the initially traversed routes are refined using 2-opt algorithms, simulated annealing, and other metaheuristics. A comparison of the algorithms used and an illustration of their operation are given.

关键词： metaheuristic algorithm multiagent optimization problem discrete optimization pseudo-Boolean problem

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metaheuristic algorithms for Proactive and Reactive Project Scheduling to Minimize Contractor's Cash Flow Gap under Random Activity Duration

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IEEE ACCESS 2018年 6卷 30547-30558页

作者： Ning, Minjing He, Zhengwen Wang, Nengmin Liu, Renjing Xi An Jiao Tong Univ Sch Management Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Proc Control & Efficiency Engn Xian 710049 Shaanxi Peoples R China ERC Proc Min Mfg Serv Shaanxi Prov Xian 710049 Shaanxi Peoples R China

This paper investigates the project scheduling problem to minimize the maximum cash flow gap of contractors under random activity duration. A comprehensive optimization model with time buffers added to the baseline schedule by project proactive scheduling and the schedule adjustment cost determined by project reactive scheduling is constructed to manage disruptions caused by the randomness of activity durations. Due to the problem's intractability, two hybrid metaheuristic algorithms, namely, tabu simulated annealing (tabu-SA) and variable neighborhood tabu search (VNTS), are developed, and several improvement measures are proposed to enhance the algorithms' performance. Based on a randomly generated data set, a computational experiment is performed to evaluate the algorithms, with the effects on results of certain key parameters also analyzed. The conclusions are as follows: tabu-SA outperforms VNTS for large-scale problems, however, the reverse holds for small-scale problems. The key parameters, including the project deadline, the cost per time buffer, the activity instability weight, the activity duration variability, the number of payments, and the payment proportion can exert important effects on the contractor's maximum cash flow gap.

关键词： Proactive and reactive project scheduling cash flow gap optimization model metaheuristic algorithm random activity duration

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Crowdsourcing Home Healthcare Service: Matching Caretakers With Caregivers for Jointly Rostering and Routing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第1期18卷 126-139页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Liu, Pei-Yu Natl Yang Ming Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan Natl Chin Yi Univ Technol Int Master Program Smart Mfg & Appl Informat Sci Taichung 411 Taiwan Chung Yuan Christian Univ Dept Ind & Syst Engn Taoyuan 320 Taiwan

In this work, we introduce crowdsourcing home healthcare service (CHHS) systems, where caregivers (including nurses, personal care attendants, and housekeepers) from different locations (rather than centralized institutions) offer diverse home healthcare services to caretakers at home. Powered by cloud computing, the CHHS system enables real-time, dynamic, and large-scale matching between caretakers and caregivers based on their preferences and constraints, and determines caregivers' rostering and routing plans, involving the NP-hard nurse rostering problem (NRP) and the vehicle routing problem (VRP). This work firstly creates a mathematical programming model to jointly roster and route for the CHHS, maximizing the matching scores of caregivers and caretakers based on the preferred features through analytic hierarchy process (AHP), and minimizing caregivers' overtime and routing costs, under constraints of caregiver skills, regulations, and vehicle routing. The proposed matching score mechanism assigns weights to caretaker preferences, enhancing pairing with preferred caregivers and reducing dissatisfaction. This work proposes a hybrid genetic algorithm with variable neighborhood search (GAVNS), respectively tailored to handle the rostering and routing aspects of CHHS. Simulation indicates that the GAVNS lowers costs by approximately 38% and 26% in rural and city cases, respectively, and outperforms standalone GA and VNS, achieving a 3% additional cost reduction and consistently yielding feasible solutions.

关键词： Routing Crowdsourcing Costs Vehicle routing Genetic algorithms Resource management Real-time systems Mathematical models Hospitals Vehicle dynamics Home healthcare service crowdsourcing rostering vehicle routing problem metaheuristic algorithm

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