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Application of swarm intelligence algorithm on PFSP 认领 引用
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作者 LI Minnan LIU Sheng 《International English Education Research》 2017年第2期52-55,共4页
With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, s... With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, scheduling reasonable flow shop production systems can improve productivity and equipment utilization rate, reduce production costs. So the production system of flow shop scheduling problem has become one of the core problems ofmanufactaring enterprises the use of more and more. 展开更多
关键词 PFSP swarm intelligence algorithm combinatorial optimiTafion industrial manufacturing
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Multi-objective scheduling of relief logistics based on swarm intelligence algorithms and spatio-temporal traffic flow 认领 引用 被引量：3
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作者 Zhiming Ding Zilin Zhao +1 位作者 Detian Liu Yang Cao 《Journal of Safety Science and Resilience》 EI CSCD 2021年第4期222-229,共8页
Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient rescue.Taking rescue time,scheduling cost and demanders’satisfac... Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient rescue.Taking rescue time,scheduling cost and demanders’satisfac-tion as goals,in this paper,an emergency supplies scheduling model based on multi-objective optimization was proposed to provide a wealth of decision-making information.Then four multi-objective optimization algorithms are employed to obtain the optimal set of scheduling models.In addition,we design the minimum time cost model and the shortest route cost model by considering the change of the road network status.The extensive simulation experiments are conducted on a real urban traffic dataset.The experimental results show that the two cost models can serve different scheduling needs and provide efficient scheduling for emergency supplies. 展开更多
关键词 Logistics scheduling MOP Swarm intelligence algorithms Spatio-temporal trajectory Traffic flow
Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL 认领 引用 被引量：5
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作者 Weiqiang Jin Xingwu Tian +3 位作者 Bohang Shi Biao Zhao Haibin Duan Hao Wu 《Computers, Materials & Continua》 SCIE EI 2024年第9期3523-3553,共31页
TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving i... TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions. 展开更多
关键词 UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training
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Image Forgery Detection Using Segmentation and Swarm Intelligent Algorithm 认领 引用 被引量：2
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作者 ZHAO Fei SHI Wenchang +1 位作者 QIN Bo LIANG Bin 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2017年第2期141-148,共8页
Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm ... Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region. 展开更多
关键词 copy-move forgery detection scale invariant features transform （SIFT） swarm intelligent algorithm particle swarm optimization
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Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network 认领 引用 被引量：2
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作者 Hao Dong Xiaohui Zhao +2 位作者 Liangdong Qu Xuefen Chi Xinyu Cui 《Journal of Bionic Engineering》 SCIE EI CSCD 2014年第3期490-496,共7页
This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network(VANET).A novel bionic swarm intelligence algorithm,which is called ant colony algorithm,was intro... This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network(VANET).A novel bionic swarm intelligence algorithm,which is called ant colony algorithm,was introduced into a traditional ad-hoc route algorithm named AODV.Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units,the parameters in ant colony system were modified to enhance the performance of the route selection probability rules.When the vehicle moves into the range of several different roadsides,it could build the route by sending some route testing packets as ants,so that the route table can be built by the reply information of test ants,and then the node can establish the optimization path to send the application packets.The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm,especially when the vehicle is in higher speed or the number of nodes increases. 展开更多
关键词 multi-hop routing optimization ant colony algorithm VANET bionic swarm intelligence algorithm
Multi-trial Vector-based Whale Optimization Algorithm 认领 引用 被引量：1
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作者 Mohammad H.Nadimi-Shahraki Hajar Farhanginasab +2 位作者 Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第3期1465-1495,共31页
The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited... The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems.To address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of difficulty.MTV-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and exploitation.For the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions.Moreover,MTV-WOA is compared with well-stablished,recent,and WOA variant algorithms.The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate.Additionally,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms.Finally,we solved five engineering design problems to demonstrate the practicality of MTV-WOA.The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms. 展开更多
关键词 Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm
Improvement of the Firework Algorithm for Classification Problems 认领 引用
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作者 Yu Xue Sow Alpha Amadou Yan Zhao 《Journal of Cyber Security》 2020年第4期191-196,共6页
Attracted numerous analysts’consideration,classification is one of the primary issues in Machine learning.Numerous evolutionary algorithms(EAs)were utilized to improve their global search ability.In the previous year... Attracted numerous analysts’consideration,classification is one of the primary issues in Machine learning.Numerous evolutionary algorithms(EAs)were utilized to improve their global search ability.In the previous years,many scientists have attempted to tackle this issue,yet regardless of the endeavors,there are still a few inadequacies.Based on solving the classification problem,this paper introduces a new optimization classification model,which can be applied to the majority of evolutionary computing(EC)techniques.Firework algorithm(FWA)is one of the EC methods,Although the Firework algorithm(FWA)is a proficient algorithm for solving complex optimization issue.The proficient of the FWA isn't fulfilled when being utilized for solving the classification issues.In this paper we previously proposed optimization classification model according to the classification issue.At that point we legitimately utilize the model with FWA to solve the classification issue.Finally,to investigate the performance of our model,we select 4 datasets in the experiments,and the results indicate that an improved FWA can upgrade the classification accuracy by using this model. 展开更多
关键词 Swarm intelligence algorithm firework algorithm evolutionary algorithm classification
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A Novel Control Algorithm for Interaction Between Surface Waves and A Permeable Floating Structure 认领 引用 被引量：1
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作者 Pei-Wei TSAI A.ALSAEDI +1 位作者 T.HAYAT Cheng-Wu CHEN 《China Ocean Engineering》 SCIE EI CSCD 2016年第2期161-176,共16页
An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment.In the design procedure of the contr... An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment.In the design procedure of the controller,a parallel distributed compensation（PDC） scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers.A stability analysis is carried out for a real structure system by using Lyapunov method.The corresponding boundary value problems are then incorporated into scattering and radiation problems.They are analytically solved,based on separation of variables,to obtain series solutions in terms of the harmonic incident wave motion and surge motion.The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width,thickness and mass has been thus drawn with a parametric approach.From which mathematical models are applied for the wave-induced displacement of the surge motion.A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system,but has the robustness against external disturbance. 展开更多
关键词 fuzzy Lyapunov function Takagi?Sugeno form swarm intelligence algorithm
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INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS 认领 引用 被引量：5
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作者 MU Bin ZHANG Lin-lin +4 位作者 YUAN Shi-jin QIAN Yi-wen WEN Shi-cheng YAN Jing-hao ZHOU Fei-fan 《Journal of Tropical Meteorology》 SCIE 2019年第1期63-81,共19页
Some intelligent algorithms(IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane(ZC) model to solve conditional nonlinear optimal perturbation(CNOP) for studying El ... Some intelligent algorithms(IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane(ZC) model to solve conditional nonlinear optimal perturbation(CNOP) for studying El Ni?o-Southern Oscillation(ENSO) predictability. Compared to the adjoint-based method(the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns.Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization(PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy(CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model(MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models. 展开更多
关键词 CNOP single individual intelligent algorithms swarm intelligent algorithms ENSO predictability tropical cyclone adaptive observations
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Product quality prediction based on RBF optimized by firefly algorithm 认领 引用 被引量：4
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作者 HAN Huihui WANG Jian +1 位作者 CHEN Sen YAN Manting 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第1期105-117,共13页
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred... With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality. 展开更多
关键词 product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm
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Whale Optimization Algorithm Strategies for Higher Interaction Strength T-Way Testing 认领 引用
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作者 Ali Abdullah Hassan Salwani Abdullah +1 位作者 Kamal Z.Zamli Rozilawati Razali 《Computers, Materials & Continua》 SCIE EI 2022年第10期2057-2077,共21页
Much of our daily tasks have been computerized by machines and sensors communicating with each other in real-time.There is a reasonable risk that something could go wrong because there are a lot of sensors producing a... Much of our daily tasks have been computerized by machines and sensors communicating with each other in real-time.There is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of data.Combinatorial testing(CT)can be used in this case to reduce risks and ensure conformance to specifications.Numerous existing metaheuristic-based solutions aim to assist the test suite generation for combinatorial testing,also known as t-way testing(where t indicates the interaction strength),viewed as an optimization problem.Much previous research,while helpful,only investigated a small number of interaction strengths up to t=6.For lightweight applications,research has demonstrated good fault-finding ability.However,the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of data.Due to resource restrictions and the combinatorial explosion challenge,little work has been done to produce high-order interaction strength.In this context,the Whale Optimization Algorithm(WOA)is proposed to generate high-order interaction strength.To ensure that WOA conquers premature convergence and avoids local optima for large search spaces(owing to high-order interaction),three variants of WOA have been developed,namely Structurally Modified Whale Optimization Algorithm(SWOA),Tolerance Whale Optimization Algorithm(TWOA),and Tolerance Structurally Modified Whale Optimization Algorithm(TSWOA).Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-thearts based strategies. 展开更多
关键词 Software testing optimization problem swarm intelligence algorithm combinatorial optimization IoT
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Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms 认领 引用
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作者 Zhonghai Jiang Qian Wang +1 位作者 Liangbing Zhou Chun Xiao 《Fluid Dynamics & Materials Processing》 EI 2023年第6期1693-1708,共16页
A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.... A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model. 展开更多
关键词 Asphalt pavement temperature field swarm intelligence optimization algorithm prediction
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Review and Classification of Bio-inspired Algorithms and Their Applications 认领 引用 被引量：5
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作者 Xumei Fan William Sayers +3 位作者 Shujun Zhang Zhiwu Han Luquan Ren Hassan Chizari 《Journal of Bionic Engineering》 SCIE EI CSCD 2020年第3期611-631,共21页
Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems.The study of bionics bridges the functions,biological structures and functions and organizat... Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems.The study of bionics bridges the functions,biological structures and functions and organizational principles found in nature with our modem technologies,numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies.Output of bionics study includes not only physical products,but also various optimization computation methods that can be applied in different areas.Related algorithms can broadly be divided into four groups:evolutionary based bio-inspired algorithms,swarm intelligence-based bio-inspired algorithms,ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms.Bio-inspired algorithms such as neural network,ant colony algorithms,particle swarm optimization and others have been applied in almost every area of science,engineering and business management with a dramatic increase of number of relevant publications.This paper provides a systematic,pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms,swarm intelligence based bio-inspired algorithms,ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms. 展开更多
关键词 bio-inspired optimization multi-objective optimization evolutionary based algorithms swarm intelligence based algorithms ecology based bio-inspired agorithms
Predicting Academic Performance Levels in Higher Education:A Data-Driven Enhanced Fruit Fly Optimizer Kernel Extreme Learning Machine Model 认领 引用 被引量：1
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作者 Zhengfei Ye Yongli Yang +1 位作者 Yi Chen Huiling Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期1940-1962,共23页
Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T... Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process. 展开更多
关键词 Academic achievement Machine learning Teacher-student relationships Swarm intelligence algorithms Fruit fly optimization algorithm
Research on swarm intelligence optimization algorithm 认领 引用 被引量：8
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作者 Fei Wei Liu Cong Hu Sheng 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2020年第3期1-20,30,共20页
The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem.It simulates the group behavior of var... The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem.It simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent individuals.This paper first introduces the principles of various swarm intelligent optimization algorithms.Then,the typical application of these swarm intelligence optimization algorithms in various fields is listed.After that,the advantages and defects of all swarm intelligence optimization algorithms are summarized.Next,the improvement strategies of various swarm intelligence optimization algorithms are explained.Finally,the future development of various swarm intelligence optimization algorithms is prospected. 展开更多
关键词 bionics natural heuristic algorithm swarm intelligence algorithm intelligent computing
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An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems 认领 引用
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作者 L. Q. Zhang J. Xiong J. K. Liu 《Journal of Applied Mathematics and Physics》 2023年第11期3641-3650,共10页
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ... In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance. 展开更多
关键词 Swarm Intelligent Optimization Algorithm Fruit Fly Optimization Algorithm Adaptive Step Local Optimum Convergence Speed
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Interpretable CEEMDAN-SMA-LSSVM hybrid model for predicting shield tunnel-induced settlement 认领 引用
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作者 Shaoqiang Meng Zhenming Shi Marte Gutierrez 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第10期6179-6194,共16页
Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid mode... Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),slime mold algorithm(SMA),and least squares support vector machine(LSSVM)to enhance prediction accuracy and model transparency.The CEEMDAN method,optimized by SMA,decomposes settlement data into intrinsic mode functions(IMFs)and residuals,thereby reducing data noise.The LSSVM,also optimized by SMA,is then applied to predict each IMF and residual.The final settlement prediction is derived from the aggregation of these results.The model was rigorously validated using the Changsha(China)and Singapore Metro projects,demonstrating superior performance to traditional machine learning models.The evaluation metrics,including root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2),underscore the model's effectiveness.The model achieved the lowest error rates and highest accuracy across these metrics.Notably,Shapley additive explanations(SHAP)provided insights into the model's decision-making process,identifying shield stoppage and moisture content as the most influential factors in settlement prediction.This study contributes to the advancement of the methodological framework for predicting tunnel settlement.It addresses the discrepancy between prediction accuracy and interpretability,providing a robust tool for practical engineering applications. 展开更多
关键词 Machine learning Geotechnical engineering Swarm intelligence optimization algorithm Shapley additive explanations(SHAP)
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Coordinated Path Planning for UAVs Based on Sheep Optimization 认领 引用 被引量：5
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作者 YANG Liuqing WANG Pengfei ZHANG Yong 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第5期816-830,共15页
Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path plann... Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved. 展开更多
关键词 multi-UAV cooperation path planning swarm intelligence algorithm multi-population improved sheep optimization(ISO)
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Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing 认领 引用 被引量：3
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作者 Jiaochen Chen Zhennao Cai +4 位作者 Huiling Chen Xiaowei Chen José Escorcia-Gutierrez Romany F.Mansour Mahmoud Ragab 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第5期2240-2275,共36页
Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopa... Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images. 展开更多
关键词 Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm Swarm intelligence algorithms Bionic algorithm
Discrete Improved Grey Wolf Optimizer for Community Detection 认领 引用 被引量：2
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作者 Mohammad H.Nadimi-Shahraki Ebrahim Moeini +1 位作者 Shokooh Taghian Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第5期2331-2358,共28页
Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively ... Detecting communities in real and complex networks is a highly contested topic in network analysis.Although many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world networks.Thus,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different networks.In the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better solution.Then a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection problem.The performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network datasets.The experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon tests.The comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms. 展开更多
关键词 Community detection Complex network Optimization Metaheuristic algorithms Swarm intelligence algorithms Grey wolf optimizer algorithm
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