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Introduction to the Special Issue on Scientific Computing and Its Application to Engineering Problems 认领 引用
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作者 Higinio Ramos M Chandru 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期22-24,共3页
Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically i... Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically intractable can now be studied through accurate simulations and large-scale numerical experiments.Scientific computing provides a bridge between mathematical theory,computational algorithms,and real-world engineering applications,enabling researchers to model complex phenomena such as fluid flow,structural deformation,wave propagation,stochastic processes,and nonlinear dynamical systems.The special issue entitled“Scientific Computing and Its Application to Engineering Problems”was conceived to bring together high-quality research contributions that demonstrate the role of computational mathematics in solving challenging engineering problems.The issue highlights both theoretical advances in numerical methods and their practical deployment in real-world engineering scenarios,with emphasis on robustness,computational efficiency,stability,and scalability. 展开更多
关键词 scientific computing accurate simulations fluid flows numerical algorithmsproblems computational power numerical algorithms engineering problems model complex phenomena
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An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem 认领 引用 被引量：6
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作者 Feyza AltunbeyÖzbay ErdalÖzbay Farhad Soleimanian Gharehchopogh 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第11期1067-1110,共44页
Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems... Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms. 展开更多
关键词 Artificial rabbit optimization binary optimization breast cancer chaotic local search engineering design problem opposition-based learning
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An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems 认领 引用 被引量：1
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作者 Mohammad H.Nadimi-Shahraki 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第3期1361-1385,共25页
Hybrid metaheuristic algorithms play a prominent role in improving algorithms'searchability by combining each algorithm's advantages and minimizing any substantial shortcomings.The Quantum-based Avian Navigati... Hybrid metaheuristic algorithms play a prominent role in improving algorithms'searchability by combining each algorithm's advantages and minimizing any substantial shortcomings.The Quantum-based Avian Navigation Optimizer Algorithm(QANA)is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds.Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields.However,it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems.Therefore,instead of proposing a new algorithm to solve these weaknesses,we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA.The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm(HQANA)is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions,30,50,and 100.The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms,including PSO,KH,GWO,WOA,CSA,HOA,BO,and QANA.Ultimately,five constrained engineering optimization problems from the latest test suite,CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems.The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms. 展开更多
关键词 Optimization Metaheuristic algorithms Evolutionary algorithm Quantum-based avian navigation optimizer algorithm Engineering optimization problems,Bionic algorithm
Introduction to the Special Issue on Applied Artificial Intelligence:Advanced Solutions for Engineering Real-World Challenges 认领 引用
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作者 Siamak Talatahari Amin Beheshti 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期39-41,共3页
This special issue,entitled“Applied Artificial Intelligence:Advanced Solutions for Engineering Real-World Challenges”,was established to highlight the practical and emerging impact of AI in addressing challenging en... This special issue,entitled“Applied Artificial Intelligence:Advanced Solutions for Engineering Real-World Challenges”,was established to highlight the practical and emerging impact of AI in addressing challenging engineering problems[1-5].The published contributions demonstrate that AI is no longer limited to theoretical investigation but is increasingly serving as a deployment-oriented tool for solving problems in infrastructure monitoring,renewable energy prediction,robotic interaction,communication systems,image reconstruction,cybersecurity,and intelligent power management.Collectively,the accepted papers show how advanced AI methodologies can be adapted to diverse engineering contexts while maintaining strong relevance to practical applications. 展开更多
关键词 infrastructure monitoring renewable energy prediction artificial intelligence advanced engineering problems interactioncommunication systems real world challenges infrastructure monitoringrenewable artificial intelligence
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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems 认领 引用 被引量：4
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作者 Jeffrey O.Agushaka Absalom E.Ezugwu +3 位作者 Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第3期1263-1295,共33页
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but... This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms. 展开更多
关键词 Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems
A Multi-strategy Improved Snake Optimizer Assisted with Population Crowding Analysis for Engineering Design Problems 认领 引用
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作者 Lei Peng Zhuoming Yuan +4 位作者 Guangming Dai Maocai Wang Jian Li Zhiming Song Xiaoyu Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第3期1567-1591,共25页
Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,... Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality. 展开更多
关键词 Snake optimizer Multi-strategy Population crowding analysis Engineering design problem
CQFFA:A Chaotic Quasi-oppositional Farmland Fertility Algorithm for Solving Engineering Optimization Problems 认领 引用 被引量：6
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作者 Farhad Soleimanian Gharehchopogh Mohammad H.Nadimi-Shahraki +2 位作者 Saeid Barshandeh Benyamin Abdollahzadeh Hoda Zamani 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第1期158-183,共26页
Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)... Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation.The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks.Therefore,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions.Furthermore,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA algorithm.The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component.The results obtained show that the proposed CQFFA model has been significantly improved.It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually.Also,the CQFFA algorithm has evaluated six real-world engineering problems.The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms. 展开更多
关键词 Nature-inspired algorithm Farmland fertility algorithm Chaotic maps Quasi Engineering optimization problems
Comparative Study on Results of Euler,Improved Euler and Run­ge-Kutta Methods for Solving the Engineering Unknown Problems 认领 引用
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作者 Khaing Khaing Lwin 《Journal of International Education and Practice》 2020年第3期1-6,共6页
The paper presents the comparative study on numerical methods of Euler method,Improved Euler method and fourth-order Runge-Kutta method for solving the engineering problems and applications.The three proposed methods ... The paper presents the comparative study on numerical methods of Euler method,Improved Euler method and fourth-order Runge-Kutta method for solving the engineering problems and applications.The three proposed methods are quite efficient and practically well suited for solving the unknown engineering problems.This paper aims to enhance the teaching and learning quality of teachers and students for various levels.At each point of the interval,the value of y is calculated and compared with its exact value at that point.The next interesting point is the observation of error from those methods.Error in the value of y is the difference between calculated and exact value.A mathematical equation which relates various functions with its derivatives is known as a differential equation.It is a popular field of mathematics because of its application to real-world problems.To calculate the exact values,the approximate values and the errors,the numerical tool such as MATLAB is appropriate for observing the results.This paper mainly concentrates on identifying the method which provides more accurate results.Then the analytical results and calculates their corresponding error were compared in details.The minimum error directly reflected to realize the best method from different numerical methods.According to the analyses from those three approaches,we observed that only the error is nominal for the fourth-order Runge-Kutta method. 展开更多
关键词 Numerical Method Euler Method Improved Euler Method Runge-Kutta Method Solving the Engineering Problems
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A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems 认领 引用 被引量：5
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作者 Elif Varol Altay Osman Altay Yusuf Ovik 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期1039-1094,共56页
Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ... Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions. 展开更多
关键词 Metaheuristic optimization algorithms real-world engineering design problems multidisciplinary design optimization problems
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Engineering for problem solving in future: Eco-social market economy and eco-social tech 认领 引用
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《Journal of Environmental Sciences》 SCIE EI CAS 2001年第3期257-264,共8页
关键词 Eco-social market economy and eco-social tech Engineering for problem solving in future In
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Genechip engineering and its related bioinformatics problems 认领 引用
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《Chinese Journal of Biomedical Engineering(English Edition)》 CAS 2002年第1期40-42,共3页
关键词 high Genechip engineering and its related bioinformatics problems DNA
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Analysis of Common Geotechnical Investigation Problems in Design Slope Support Engineering 认领 引用
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作者 QIANXueyi 《外文科技期刊数据库（文摘版）工程技术》 2022年第2期157-160,共4页
Slope support project has an important role in construction engineering, which can have a direct impact on the overall quality of construction engineering. In the slope design of supporting engineering, if the staff d... Slope support project has an important role in construction engineering, which can have a direct impact on the overall quality of construction engineering. In the slope design of supporting engineering, if the staff does not effectively deal with the geotechnical engineering investigation problems, it is very likely to reduce the scientific nature of the engineering design, and affect the overall construction quality of the project. Therefore, in order to avoid the above phenomena and promote the healthy development of construction engineering, this paper analyzes the common geotechnical investigation problems and solution measures in the design of slope support engineering through the actual investigation and analysis, so as to provide a reliable basis for the designers to carry out their work. 展开更多
关键词 slope support engineering design geotechnical engineering survey problems analysis
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APFA:Ameliorated Pathfinder Algorithm for Engineering Applications 认领 引用 被引量：1
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作者 Keyu Zhong Fen Xiao Xieping Gao 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第3期1592-1616,共25页
Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food s... Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food source.This algorithm has the characteristics of a simple structure and high performance.However,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities.This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization problems.Firstly,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm.Secondly,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions.Thirdly,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed.These three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the algorithm.In performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic algorithms.The numerical experimental results demonstrated the superiority of APFA. 展开更多
关键词 Pathfinder algorithm Swarm intelligent Metaheuristic Engineering problems
African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with Engineering Applications 认领 引用
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作者 Jian Zhao Kang Wang +2 位作者 Jiacun Wang Xiwang Guo Liang Qi 《Computers, Materials & Continua》 SCIE EI 2024年第10期603-623,共21页
This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and ... This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and eliminating.The foraging behavior prompts the bison to seek a richer food source for survival.When bison find a food source,they stick around for a while by bathing behavior.The jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating behavior.The eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent individuals.The above behaviors are translated into ABO by mathematical modeling.To assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with constraints.The findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms. 展开更多
关键词 Optimization metaheuristics African bison optimization engineering problems
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A Curriculum Cluster Teaching Model of Software Engineering from the Metaverse Perspective 认领 引用
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作者 Rui Ding Rao Ma +1 位作者 Jiahui Liao Xiaodong Wang 《计算机教育》 2022年第12期143-151,共9页
To solve the teaching difficulties,including hard cultivating engineering thinking,a reasonable transition of professional training,and deep cooperation of students,which impeded the cultivation effectiveness of stude... To solve the teaching difficulties,including hard cultivating engineering thinking,a reasonable transition of professional training,and deep cooperation of students,which impeded the cultivation effectiveness of students’ability to solve complex engineering problems,the paper proposed a Zongheng group teaching model of curriculum cluster based on projects.Firstly,from the perspective of Metaverse,and considering the professional,current teaching situation and learning situation,we analyzed the professional background and proposed the Zongheng group teaching model of curriculum cluster.Then,the connotation,teaching construction and implementation details are explained.After that,we summarized the teaching effect about the 2 years of exploration and practice in the major of software engineering at College of Computer and Information Technology in Mudanjiang Normal University,to clarify the effect of teaching reform.2 years of teaching practice shows that making full use of the advantages of curriculum cluster,Zongheng group and the project-based teaching method,the long-range training,in-depth student cooperation,and the students’ability of solving complex engineering problems are improved. 展开更多
关键词 Metaverse Engineering education professional certification Complex engineering problems Curriculum cluster construction Modern education technology
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An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization 认领 引用 被引量：6
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作者 Wenchuan Wang Weican Tian +3 位作者 Kwok-wing Chau Yiming Xue Lei Xu Hongfei Zang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1603-1642,共40页
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta... The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems. 展开更多
关键词 Bald eagle search algorithm cauchymutation adaptive weight factor CEC2017 benchmark functions engineering optimization problems
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Enhanced Particle Swarm Optimization Algorithm Based on SVM Classifier for Feature Selection 认领 引用
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作者 Xing Wang Huazhen Liu +2 位作者 Abdelazim G.Hussien Gang Hu Li Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2791-2839,共49页
Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of t... Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems. 展开更多
关键词 Feature selection SVM particle swarm optimization sand cat swarm optimization engineering problems
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An Information Theory for the Geotechnical Engineering Inverse Problem 认领 引用
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《岩石力学与工程学报》 CAS 1995年第S1期473-483,共11页
AnInformationTheoryfortheGeotechnicalEngineeringInverseProblemByLiuWeining（DepartmentofCivilEngineering，NorthJiaotongUniversi...
关键词 An Information Theory for the Geotechnical Engineering Inverse Problem
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Features of Tropical Volcanic Rock and Soil of Jakarta-Bandung HSR and Engineering Countermeasures 认领 引用
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作者 ZHAO Dou WANG Shujie ZHENG Mingda 《Chinese Railways》 2024年第1期30-37,共8页
The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through fie... The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results. 展开更多
关键词 Jakarta-Bandung HSR tropical volcanic rock and soil engineering geological features engineering geological problems engineering countermeasures
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The Colony Predation Algorithm 认领 引用 被引量：40
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作者 Jiaze Tu Huiling Chen +1 位作者 Mingjing Wang Amir H.Gandomi 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第3期674-710,共37页
This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by anima... This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication. 展开更多
关键词 Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems
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