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Theory Evolution Optimization:A Metaheuristic Algorithm BaSed on Evolution Process of Theory 认领 引用
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作者 Jiacong Liu Jiaze Tu +5 位作者 Chunguang Bi Huiling Chen Ali Asghar Heidari Hao Xie Lei Liu Yi Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1015-1060,共46页
Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the T... Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the Theory Evolution Optimization(TEO)–an efficient metaheuristic inspired by the evolution of scientific theory.TEO simulates the competitive,accumulative,and replacement processes among scientific hypotheses,mirroring the evolution from a hypothesis to an established scientific theory.The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms,including highly competitive champions such as EBOwithCMAR,LSHADE_cnEpSi,and LSHADE.Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test,with multiple comparisons managed by the Friedman test.Initially,TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions.TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications.Additionally,a binary TEO(BTEO)variant is introduced and applied to feature selection tasks in discrete space.Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems.The source codes for this research are accessible to the public at http://gffzze767f4cc5ce545d8s6bkbn0ppfn0u6qop.ffgz.tsg.suse.edu.cn/TEO.html. 展开更多
关键词 Optimization,metaheuristic algorithms Evolution of scientific theory Theory evolution optimizers Engineering design optimization Feature selection
Painted Wolf Optimization:A Novel Nature-Inspired Metaheuristic Algorithm for Real-World Optimization Problems 认领 引用
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作者 Saeid Sheikhi 《Computers, Materials & Continua》 SCIE EI 2026年第5期243-271,共29页
Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.T... Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization(PWO)algorithm.The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves,also known as African wild dogs in the wild,particularly their unique consensus-based voting rally mechanism,a behavior fundamentally distinct fromthe social dynamics of grey wolves.In this innovative process,pack members explore different areas to find prey;then,they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey.The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions,including the Congress on Evolutionary Computation(CEC)2017 suite and different real-world engineering design cases.Furthermore,the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces.This includes its application within the field of cybersecurity,specifically in the context of training a machine learning-based intrusion detection system(ML-IDS),achieving an accuracy of 0.90 and an F-measure of 0.9290.Statistical analyses using the Wilcoxon signed-rank test(all p<0.05)indicate that the PWO algorithm outperforms existing state-of-the-art algorithms,providing superior solutions in diverse and unpredictable optimization landscapes.This demonstrates its potential as a robust method for tackling complex optimization problems in various fields.The source code for thePWOalgorithmis publicly available at http://gffzz188fe103f8f1460as6bkbn0ppfn0u6qop.ffgz.tsg.suse.edu.cn/saeidsheikhi/Painted-Wolf-Optimization. 展开更多
关键词 Optimization painted wolf optimization algorithm metaheuristic algorithm nature-inspired computing swarm intelligence
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Pareto Multi-Objective Reconfiguration of IEEE 123-Bus Unbalanced Power Distribution Networks Using Metaheuristic Algorithms:A Comprehensive Analysis of Power Quality Improvement 认领 引用 被引量：1
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作者 Nisa NacarÇıkan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期3279-3327,共49页
This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r... This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management. 展开更多
关键词 Voltage and current unbalanced index unbalanced power distribution network power quality metaheuristic algorithms reconfiguration optimization
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Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms 认领 引用 被引量：15
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作者 Chuanqi Li Jian Zhou +1 位作者 Kun Du Daniel Dias 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2023年第8期1019-1036,共18页
Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safet... Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability. 展开更多
关键词 Underground pillar stability Hard rock Support vector machine Metaheuristic algorithms
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A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System 认领 引用 被引量：4
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作者 Omar Almomani 《Computers, Materials & Continua》 SCIE EI 2021年第7期409-429,共21页
Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features s... Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features selection approach plays an important role in constructing effective network IDS.Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy.Therefore,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack.The proposed model has two objectives;The first one is to reduce the number of selected features for Network IDS.This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model.The algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning classifiers.This objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)classifiers.UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model.UNSW-NB15 dataset has nine attacks type.The generic attack is the highest among them.Therefore,the proposed model aims to identify generic attacks.My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model.In terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers. 展开更多
关键词 IDS metaheuristic algorithms PSO MVO GWO MFO WOA FFA BAT SVM J48 RF UNSW-NB15 dataset
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Reactions’Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble 认领 引用
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作者 Olutomilayo Olayemi Petinrin Faisal Saeed +2 位作者 Xiangtao Li Fahad Ghabban Ka-Chun Wong 《Computers, Materials & Continua》 SCIE EI 2022年第3期4745-4762,共18页
Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessa... Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessary for the estimation of yields in these reactions.This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation.The algorithms were evaluated based on computational time and the number of selected descriptors.Analyses show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are selected.The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R2 of 0.9423.The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation.The ensemble model has also shown robust performance in its yield estimation ability and efficiency. 展开更多
关键词 Buchwald-Hartwig reaction descriptor selection machine learning metaheuristic algorithm palladium-catalyzed cross-coupling reaction voting ensemble
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IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification 认领 引用
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作者 Sarab AlMuhaideb Reem BinGhannam +3 位作者 Nourah Alhelal Shatha Alduheshi Fatimah Alkhamees Raghad Alsuhaibani 《Computers, Materials & Continua》 SCIE EI 2021年第2期1329-1346,共18页
Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to ... Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively). 展开更多
关键词 Ant colony optimization AntMiner+ IWDs IWD-Miner J48 medical data classification metaheuristic algorithms swarm intelligence
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Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems 认领 引用 被引量：5
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作者 Pavel Trojovsky Mohammad Dehghani +1 位作者 Eva Trojovská Eva Milkova 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1527-1573,共47页
In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education O... In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test suite.The optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications. 展开更多
关键词 Optimization language education exploration exploitation metaheuristic algorithm
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Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm 认领 引用 被引量：2
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作者 Said Ali Hassan Khalid Alnowibet +1 位作者 Prachi Agrawal Ali Wagdy Mohamed 《Computers, Materials & Continua》 SCIE EI 2021年第7期1183-1202,共20页
Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 20... Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space. 展开更多
关键词 Facility location nonlinear binary model field hospitals for COVID-19 gaining-sharing knowledge-based metaheuristic algorithm
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2-D DOA Estimation in a Cuboid Array Based on Metaheuristic Algorithms and Maximum Likelihood 认领 引用 被引量：1
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作者 Gilberto Lopes Filho Ana Cláudia Barbosa Rezende +2 位作者 Lucas Fiorini Cruz Flávio Henrique Teles Vieira Rodrigo Pinto Lemos 《International Journal of Communications, Network and System Sciences》 2020年第8期121-137,共17页
This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is es... This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm. 展开更多
关键词 Metaheuristic Algorithms Genetic Algorithm Firefly Algorithm DOA Estimation Maximum Likelihood
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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Structured Random Cycle-Guided Algorithm (SRCA): An Adaptive Metaheuristic Combining Directionally-Guided and Stochastic Search Strategies 认领 引用
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作者 Giuseppe Marannano Antonino Cirello Tommaso Ingrassia 《Computers, Materials & Continua》 SCIE EI 2026年第6期1873-1898,共26页
In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).S... In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).SRCA is not presented as a fundamentally new optimization paradigm,but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection.Based on a cycle-structured architecture,directional and stochastic search behaviors are dynamically selected at the individual level.The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies,enabling a coherent and adaptive balance between exploration and exploitation throughout the optimization process.Unlike traditional metaheuristics that rely on fixed behavioral roles or static movement schemes,SRCA allows each individual to adapt its search strategy based on real-time population feedback,monitored through convergence and dispersion indicators.The performance of SRCA is quantitatively assessed under strictly identical experimental conditions on a comprehensive set of 23 benchmark functions,including multimodal and high-dimensional problems,as well as on six classical constrained engineering design problems.Numerical results demonstrate competitive convergence reliability and robustness across diverse optimization tasks,confirming the effectiveness of the proposed adaptive cycle-based framework. 展开更多
关键词 Metaheuristic algorithms optimization constrained optimization benchmark functions structured random cycle-guided algorithm
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Metaheuristic-Driven Abnormal Traffic Detection Model for SDN Based on Improved Tyrannosaurus Optimization Algorithm 认领 引用
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作者 Hui Xu Jiahui Chen Zhonghao Hu 《Computers, Materials & Continua》 SCIE EI 2025年第6期4495-4513,共19页
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 ... 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 beforemachine 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 frominsufficient 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. 展开更多
关键词 Software-defined networking abnormal traffic detection feature selection metaheuristic algorithm tyrannosaurus optimization algorithm
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Cooperative Metaheuristics with Dynamic Dimension Reduction for High-Dimensional Optimization Problems 认领 引用
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作者 Junxiang Li Zhipeng Dong +2 位作者 Ben Han Jianqiao Chen Xinxin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1484-1502,共19页
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta... Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems. 展开更多
关键词 Dimension reduction modified principal components analysis high-dimensional optimization problems cooperative metaheuristics metaheuristic algorithms
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Advanced forecasting of building energy loads with XGBoost and metaheuristic algorithms integration 认领 引用
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作者 Zuriani Mustaffa Mohd Herwan Sulaiman 《Energy Storage and Saving》 EI 2025年第4期421-438,共18页
Accurate forecasting of cooling and heating loads in buildings is vital for effective energy management,cost efficiency,and environmental sustainability.However,traditional forecasting models often face limitations in... Accurate forecasting of cooling and heating loads in buildings is vital for effective energy management,cost efficiency,and environmental sustainability.However,traditional forecasting models often face limitations in capturing the complex and non-linear characteristics of building energy consumption patterns.To address this challenge,this study proposes a hybrid predictive approach by integrating eXtreme gradient boosting(XGBoost)with eight metaheuristic optimization algorithms.The selected algorithms are ant colony optimization(ACO),barnacles mating optimizer(BMO),genetic algorithm(GA),gradient-based optimizer(GBO),hippopotamus optimization(HO),Kepler optimization algorithm(KOA),particle swarm optimization(PSO),and teachinglearning-based optimization(TLBO).Each metaheuristic was used to optimize the hyperparameters of the XGBoost model,resulting in the following hybrid models:ACO-XGBoost,BMO-XGBoost,GA-XGBoost,GBOXGBoost,HO-XGBoost,KOA-XGBoost,PSO-XGBoost,and TLBO-XGBoost.The models were evaluated based on the root mean square error(RMsE)metric to determine prediction accuracy.Among the tested combinations,the GA-XGBoost model produced the lowest RMSE for both cooling and heating load forecasting,indicating superior performance.These findings suggest that hybridizing XGBoost with metaheuristic algorithms can substantially improve forecasting accuracy.The consistent effectiveness of GA highlights its continued relevance in solving complex optimization tasks,aligning with the no free lunch theorem which states that no single algorithm performs best across all problems. 展开更多
关键词 Cooling load Heating load Hybrid algorithm Metaheuristic algorithms Machine learning
Gekko Japonicus Algorithm:A Novel Nature-inspired Algorithm for Engineering Problems and Path Planning 认领 引用
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作者 Ke Zhang Hongyang Zhao +2 位作者 Xingdong Li Chengjin Fu Jing Jin 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第1期431-471,共41页
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo... This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:http://gffzz188fe103f8f1460as6bkbn0ppfn0u6qop.ffgz.tsg.suse.edu.cn/zhy1109/Gekko-japonicusalgorithm. 展开更多
关键词 Gekko japonicus algorithm Metaheuristic algorithm Exploration and exploitation Engineering optimization Path planning
Pigeon-Inspired Optimization Algorithm:Definition,Variants,and Its Applications in Unmanned Aerial Vehicles 认领 引用
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作者 Yu-Xuan Zhou Kai-Qing Zhou +2 位作者 Wei-Lin Chen Zhou-Hua Liao Di-Wen Kang 《Computers, Materials & Continua》 SCIE EI 2026年第4期186-225,共40页
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ... ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants. 展开更多
关键词 Pigeon-inspired optimization metaheuristic algorithm algorithmvariants swarmintelligence variants UAVs convergence analysis
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Weighted k-NNC:An Efficient Computation Reduction Method for Metaheuristic-Based Structural Optimization 认领 引用
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作者 Anh-Vu Nguyen Tien-Chuong Vu +2 位作者 Ba-Duan Nguyen Hoang-Anh Pham Ravipudi Venkata Rao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期455-487,共33页
Structural optimization is essential for finding optimal designs in practical engineering tasks.Metaheuristic algorithms have been widely applied in structural optimization problems in recent years,especially when dea... Structural optimization is essential for finding optimal designs in practical engineering tasks.Metaheuristic algorithms have been widely applied in structural optimization problems in recent years,especially when dealing with discrete design variables,the nonlinearity of the objective function and constraints.Unlike gradientbased algorithms,which rely on the slope variation of a function,metaheuristic algorithms do not require derivative calculations and thus avoid being trapped in local optimum.However,metaheuristic algorithms often require numerous function evaluations,involving costly structural analyses,thus increasing computational load considerably.This paper investigates a method to reduce computational load,specifically by reducing the number of function evaluations for metaheuristic-based structural optimization problems.The proposed strategy is based on eliminating unpromising designs during the optimization process.For each newly generated solution,an early assessment through its k nearest neighbors,named k-nearest neighbor comparison(k-NNC),is applied,acting as a filter.If a solution is deemed less promising,it is eliminated without going through the function evaluation step.Conversely,if a solution is deemed good,it is retained for the next comparison and selection step.This paper presents the implementation sequence of k-NNC,highlighting its disadvantages in terms of efficiency and accuracy.From this,a new method,the distance-weighted k-nearest neighbor comparison(wkNNC),has been developed.In wkNNC,the distance from the k neighbors to the solution under consideration is used as the weight for comparison.Furthermore,an archive of infeasible solutions and the potential solution refinement are introduced for enhancing the accuracy and efficiency of wkNNC.The superiority of wkNNC is demonstrated in the sizing optimization of some benchmark discrete cross-section truss structures.The wkNNC method,combined with the Best-Worst-Random(BWR)algorithm,achieves a computational load reduction of over 80 percent. 展开更多
关键词 Structural optimization metaheuristic algorithm computation reduction k-NN distance-weighted k-NNC BWR algorithm
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Reaction process optimization based on interpretable machine learning and metaheuristic optimization algorithms 认领 引用 被引量：2
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作者 Dian Zhang Bo Ouyang Zheng-Hong Luo 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第8期77-85,共9页
The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and u... The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes. 展开更多
关键词 Reaction process optimization Interpretable machine learning Metaheuristic optimization algorithm Biodiesel
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A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms 认领 引用 被引量：13
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作者 Sajad Sabzi Yousef Abbaspour-Gilandeh Ginés García-Mateos 《Information Processing in Agriculture》 EI 2018年第1期162-172,共11页
Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identifica... Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly available.After segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image processing.Among them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of kNN.The average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of kNN.Thus,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing factories.The main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms. 展开更多
关键词 Computer vision Fruits classification Hybrid neural networks Image processing Metaheuristic algorithms
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