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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
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Multi-Objective Approach for Optimizing Production Parameters of Low-Permeability Oil Well to Enhance Energy Efficiency 认领 引用
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作者 LIU Peijin DING Haojian +3 位作者 YAN Dongyang SUN Haofeng HUANG Tao LI Jie 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期486-498,共13页
Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of product... Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of production parameters,this paper proposes a multi-objective approach for optimizing production parameters of low-permeability oil well to enhance its energy efficiency.First,a sub-model of daily liquid production yield and a sub-model of unit production energy consumption cost for single low-permeability oil well were established,and the Gaussian mixture model method was employed to compensate for the errors in the sub-model of unit production energy consumption cost,to solve the problem of the influence of uncertain facts during the oil well exploitation and to improve the precision of the model.Second,a multi-objective optimization model was established by taking into account the decision variables and constraints of the model,to maximize the daily liquid production yield while minimizing the unit production energy consumption cost.Subsequently,the non-dominated sorting genetic algorithm was employed to solve the multi-objective optimization model and obtain the production parameters.Finally,the solution set with obvious features was taken as the production parameters and applied to the actual production verification of low-permeability oil wells in a certain oil production plant of the ChangQing Oilfield.The results showed an increase in oil well production yield,and a significant energy-saving effect,thereby verifying the effectiveness of the proposed model and optimization algorithm in this paper. 展开更多
关键词 low-permeability oil well production parameter model error compensation multi-objective optimization non-dominated genetic algorithm
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Boundary Decision-Based Multi-Objective Robust Optimization for Microgrid Dispatching 认领 引用
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作者 Junjian Wu Jingliao Sun +2 位作者 Yejun Xiang Zhenyu Zhou Zhengchai Shi 《Energy Engineering》 EI 2026年第7期404-423,共20页
The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization ... The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization strategy grounded in uncertain boundary decision-making and enhanced through innovations in the multi-objective cross-entropy method.An uncertainty budget-aware environmental economic dispatch model is first established,integrating photovoltaic and wind power generation.By employing mathematical sophistication-particularly Lagrangian transformation-the proposed method effectively resolves embedded uncertainties,transforming the original model into a deterministic multi-objective optimization framework robust against renewable energy volatility.Furthermore,by incorporating the dynamic operational demands of microgrids,this paper culminates in a robust optimization approach that is both fundamentally based on and adaptively responsive to uncertainty boundaries.To address the critical challenges of convergence and diversity in multi-objective optimization,crossover operators and an adaptive parameter update mechanism are introduced,significantly refining the conventional multi-objective cross-entropy algorithm.Case studies demonstrate the rationality and effectiveness of the proposed dispatch strategy and corroborate the superior performance and applicability of the enhanced algorithm. 展开更多
关键词 Microgrid environmental economic dispatch uncertain boundary robust optimization multi-objective cross entropy algorithm
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An Improved Multi-objective Artificial Hummingbird Algorithm for Capacity Allocation of Supercapacitor Energy Storage Systems in Urban Rail Transit 认领 引用 被引量：1
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作者 Xin Wang Jian Feng Yuxin Qin 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第2期866-883,共18页
To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved... To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains. 展开更多
关键词 Multi-objective artificial hummingbird algorithm Tent mapping based on random variables Urban rail transit Supercapacitor energy storage systems Capacity allocation
A Review of the Evolution of Multi-Objective Evolutionary Algorithms 认领 引用 被引量：1
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作者 Thomas Hanne Mohammad Jahani Moghaddam 《Computers, Materials & Continua》 SCIE EI 2025年第12期4203-4236,共34页
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp... Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential. 展开更多
关键词 Multi-objective optimization evolutionary algorithms Pareto-based selection decomposition-based methods advanced analytics
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Dynamic Multi-Objective Gannet Optimization(DMGO):An Adaptive Algorithm for Efficient Data Replication in Cloud Systems 认领 引用
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作者 P.William Ved Prakash Mishra +3 位作者 Osamah Ibrahim Khalaf Arvind Mukundan Yogeesh N Riya Karmakar 《Computers, Materials & Continua》 SCIE EI 2025年第9期5133-5156,共24页
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat... Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance. 展开更多
关键词 Cloud computing data replication dynamic optimization multi-objective optimization gannet optimization algorithm adaptive algorithms resource efficiency scalability latency reduction energy-efficient computing
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A decoupled multi-objective optimization algorithm for cut order planning of multi-color garment 认领 引用
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作者 DONG Hui LYU Jinyang +3 位作者 LIN Wenjie WU Xiang WU Mincheng HUANG Guangpu 《High Technology Letters》 EI CAS 2025年第1期53-62,共10页
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish... This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises. 展开更多
关键词 multi-objective optimization non-dominated sorting in genetic algorithmsⅡ(NSGAII) cut order planning(COP) multi-color garment linear programming decoupling strategy
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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
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作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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Multi-objective optimization and biomechanical injury analysis of rear-seat occupant restraint systems in small overlap collision 认领 引用
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作者 Jia Yu Jingyu Xu +5 位作者 Tao Xiong Meng Xu Hao Deng Zhiyong Yin Xiuju Yang Shengxiong Liu 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第1期118-126,共9页
In small overlap collision,rear-seat occupants face elevated injury risks.This study conducts multi-objective opti-mization of the rear-seat occupant restraint system to reduce these injury risks and enhance overall v... In small overlap collision,rear-seat occupants face elevated injury risks.This study conducts multi-objective opti-mization of the rear-seat occupant restraint system to reduce these injury risks and enhance overall vehicle safety performance.Based on real-world traffic accident data,a full-vehicle crash simulation model was established and validated with the actual injury data.Weighted injury criteria(WIC)and neck injury metrics(Nij)were selected as optimization objectives.The rear-seat restraint system was optimized using NSGA-Ⅱ and TOPSIS algorithms to determine the optimal parameter configurations.The optimized parameters were subsequently reintegrated into the simulation model for validation.The results demonstrate a significant reduction in occupant injuries,with WIC and Nij reduced by 30.3%and 20.7%,respectively. 展开更多
关键词 Small overlap collision Rear-seat occupant injury Biomechanics analysis Multi-objective algorithm
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Constraint Intensity-Driven Evolutionary Multitasking for Constrained Multi-Objective Optimization 认领 引用
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作者 Leyu Zheng Mingming Xiao +2 位作者 Yi Ren Ke Li Chang Sun 《Computers, Materials & Continua》 SCIE EI 2026年第3期1241-1261,共21页
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red... In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs. 展开更多
关键词 Constrained multi-objective optimization evolutionary algorithm evolutionary multitasking knowledge transfer
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Two-stage wind-solar-storage capacity allocation method research for active distribution network based on whale migrating algorithm 认领 引用
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作者 Wu Zheng Ting Tang +1 位作者 Xianggang He Jierui Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期555-566,共12页
With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and co... With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and cost-effective operation of power systems.Existing research often fails to consider the interconnections among optimal dispatching and the distribution capacities of wind,solar,and energy-storage systems(ESSs).This increases the costs and dispatching difficulties.In response to this situation,a two-stage capacity-allocation approach for wind and solar power and storage in an active distribution network(ADN)is proposed in this paper.This approach is founded based on the whale migration algorithm(WMA).First,an optimization dispatch model that considers controllable loads and energy storage is formulated to minimize the dispatch operation costs of the ADN.In this optimization model,the overall cost of the ADN is taken as the objective function.The optimal configuration for wind–solar–storage capacities is obtained through the WMA.Simulation results confirm that the WMA effectively balances the solution accuracy and computational efficiency,while the proposed scheme enhances the economic performance of active distribution grids. 展开更多
关键词 Active distribution network(ADN) Optimization dispatching Capacity allocation Two-stage Whale migration algorithm(WMA)
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Optimal proportioning of iron ore in sintering process based on improved multi-objective beluga whale optimisation algorithm 认领 引用 被引量：2
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作者 Zong-ping Li Xu-dong Li +5 位作者 Xue-tong Yan Wu Wen Xiao-xin Zeng Rong-jia Zhu Ya-hui Wang Ling-zhi Yi 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2024年第7期1597-1609,共13页
Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the... Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the sintering process,a multi-objective optimisation model for sintering proportioning was established,which takes the proportioning cost and TFe as the optimisation objectives.Additionally,an improved multi-objective beluga whale optimisation(IMOBWO)algorithm was proposed to solve the nonlinear,multi-constrained multi-objective optimisation problems.The algorithm uses the con-strained non-dominance criterion to deal with the constraint problem in the model.Moreover,the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity.The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front.Meanwhile,compared with the actual proportioning scheme,the proportioning cost is reduced by 4.3361¥,and the TFe content in the mixture is increased by 0.0367%in the optimal compromise solution.Therefore,the proposed method effectively balances the cost and total iron,facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance. 展开更多
关键词 Sintering process Proportioning Iron ore Multi-objective beluga whale optimisation algorithm Proportioning cost
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Multi-Objective Evolutionary Framework for High-Precision Community Detection in Complex Networks 认领 引用
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作者 Asal Jameel Khudhair Amenah Dahim Abbood 《Computers, Materials & Continua》 SCIE EI 2026年第1期1453-1483,共31页
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r... Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification. 展开更多
关键词 Multi-objective optimization evolutionary algorithms community detection heuristic metaheuristic hybrid social network models
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Back analysis of rock mass parameters in mechanized twin tunnels based on coupled auto machine learning and multi-objective optimization algorithm 认领 引用 被引量：1
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作者 Chengwen Wang Xiaoli Liu +4 位作者 Jiubao Li Enzhi Wang Nan Hu Wenli Yao Zhihui He 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第11期7038-7055,共18页
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache... Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations. 展开更多
关键词 Back analysis of rock parameters Auto machine learning Multi-objective optimization algorithm Mechanized twin tunnels Parametric modeling
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Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems 认领 引用 被引量：1
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作者 Miloš Sedak Maja Rosic Božidar Rosic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期2111-2145,共35页
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op... This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain. 展开更多
关键词 Multi-objective optimization planetary gearbox gear efficiency sailfish optimization differential evolution hybrid algorithms
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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems 认领 引用
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作者 Nour Elhouda Chalabi Abdelouahab Attia +4 位作者 Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期967-1008,共42页
Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Op... Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization. 展开更多
关键词 Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance
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Multi-objective optimal design of asymmetric base-isolated structures using NSGA-Ⅱ algorithm for improving torsional resistance 认领 引用
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作者 Zhang Jiayu Qi Ai Yang Mianyue 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2025年第3期811-825,共15页
Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is... Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design. 展开更多
关键词 asymmetric base-isolated structures isolator arrangement multi-objective optimization NSGA-Ⅱalgorithm optimization design platform
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Stochastic multi-objective optimization for dynamic timetable and trackallocationathigh-speedrailwayhubs 认领 引用
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作者 Bing Yan Lu Huang +3 位作者 Chen Wan Siyuan Qu Xiaodong Fan Xiaolei Zou 《International Journal of Transportation Science and Technology》 EI CSCD 2026年第2期1-17,共17页
Train scheduling and track allocation are crucial for minimizing passenger flow conflicts,ensuring safety,and enhancing travel experience at railway hubs.This study presents a col-laborative optimization model for rai... Train scheduling and track allocation are crucial for minimizing passenger flow conflicts,ensuring safety,and enhancing travel experience at railway hubs.This study presents a col-laborative optimization model for railway hub operations,focusing on improving passen-ger transfer efficiency and minimizing arrival-departure track(ADT)utilization costs.A multi-objective hybrid model,referred to as COTADT,is developed to address spatiotempo-ral and stochastic constraints while balancing these two objectives.To solve this model efficiently,a customized augmented epsilon-constraint(CAEC)algorithm is introduced,utilizing augmented constraints and interval partitioning to generate Pareto-optimal solu-tions.The approach is validated with real-world data from the Hangzhou Hub,yielding sig-nificant improvements,including a 23.149%reduction in passenger transfer costs and a 7.101%reduction in ADT utilization costs.Comparative experiments demonstrate that CAEC outperforms heuristic algorithms in both solution quality and computational effi-ciency.This research provides a robust,scalable framework for enhancing operational per-formance and passenger experience at railway hubs. 展开更多
关键词 Railway hub Arrival-departure track(ADT)allocation Epsilon-constraint algorithm Multi-objective optimization Pareto optimality
Wheat Leaf Disease Detection with ANOVA-Driven Feature Selection and Whale Optimization Algorithm 认领 引用
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作者 M Chilakarao Santi Kumari Behera +1 位作者 Ashoka Kumar Ratha Prabira Kumar Sethy 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第4期73-84,共12页
Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement.In this study,we propose a novel hybrid framework that combines deep feature extraction(using Re... Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement.In this study,we propose a novel hybrid framework that combines deep feature extraction(using ResNet50 and VGG16)with ANOVA-driven feature selection and whale optimization algorithm(WOA)for hyperparameter tuning of support vector machine(SVM)classifiers.The dataset consists of 40285 wheat leaf images across eight classes(seven disease types and healthy leaves),including augmented samples to address class imbalance.The analysis of variance(ANOVA)method significantly reduced dimensionality by selecting the top 500 most relevant features,while the WOA fine-tuned the SVM to enhance classification performance.The proposed model achieved an impressive accuracy of 98.1%,along with a precision of 97.9%,a recall of 98.0%,and an F1-score of 98.0%on the independent test set.A comparative analysis shows that our method outperforms several state-of-the-art(SOTA)models,including standard CNN and ensemble approaches.This study demonstrates that combining statistical feature selection and bio-inspired optimization with deep learning can substantially advance automated wheat leaf disease detection,offering promising applications for precision agriculture. 展开更多
关键词 wheat leaf disease classification deep learning ResNet50 VGG16 support vector machine ANOVA feature selection whale optimization algorithm
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Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection 认领 引用 被引量：1
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作者 Deng Yang Chong Zhou +2 位作者 Xuemeng Wei Zhikun Chen Zheng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1563-1593,共31页
In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature sel... In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:http://gffzz188fe103f8f1460asbqx5ckbk6c5o6fp9.ffgz.tsg.suse.edu.cn/zc0315/MSMOWOA. 展开更多
关键词 Multi-objective optimization whale optimization algorithm multi-strategy feature selection
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