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A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
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作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
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Path planning of unmanned surface vehicles based on improved particle swarm optimization algorithm with consideration of particle sight distance 认领 引用
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作者 WANG Cheng YANG Junnan +3 位作者 ZHANG Xinyang QIAN Zhong ZHU Ye LIU Hong 《上海海事大学学报》 北大核心 2026年第1期9-19,共11页
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc... To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs. 展开更多
关键词 particle swarm optimization algorithm(PSO) sight distance unmanned surface vehicle(USV)
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Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm 认领 引用 被引量：9
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作者 Yan Xiang Shu-yan Fu +2 位作者 Kai Zhu Hui Yuan Zhi-yuan Fang 《Water Science and Engineering》 EI CAS CSCD 2017年第1期70-77,共8页
Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam,... Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly. 展开更多
关键词 Monitoring model Particle swarm optimization algorithm Earth rock dam Lagging effect Typhoon Seepage pressure Mutation factor Piezometric level
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Blending Scheduling under Uncertainty Based on Particle Swarm Optimization Algorithm 认领 引用 被引量：16
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作者 ZHAO Xiaoqiang +1 位作者 RONG Gang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2005年第4期535-541,共7页
Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. ... Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem. 展开更多
关键词 blending scheduling uncertainty gasoline blending particle swarm optimization algorithm nonlinear optimization
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An Improved Particle Swarm Optimization Algorithm with Harmony Strategy for the Location of Critical Slip Surface of Slopes 认领 引用 被引量：13
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作者 李亮 褚雪松 《China Ocean Engineering》 SCIE EI 2011年第2期357-364,共8页
The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy ... The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy can be adopted instead of the three parameters which are required in the original particle swarm optimization algorithm to update the positions of all the particles. The improved particle swarm optimization is used in the location of the critical slip surface of soil slope, and it is found that the improved particle swarm optimization algorithm is insensitive to the two parameters while the original particle swarm optimization algorithm can be sensitive to its three parameters. 展开更多
关键词 slope stability analysis limit equilibrium method particle swarm optimization algorithm harmony strategy
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Improved wavelet neural network combined with particle swarm optimization algorithm and its application 认领 引用 被引量：2
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作者 李翔 杨尚东 +1 位作者 乞建勋 杨淑霞 《Journal of Central South University of Technology》 2006年第3期256-259,共4页
An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learnin... An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function. 展开更多
关键词 artificial neural network particle swarm optimization algorithm short-term load forecasting wavelet curse of dimensionality
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An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application 认领 引用 被引量：2
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作者 李星梅 张立辉 +1 位作者 乞建勋 张素芳 《Journal of Central South University of Technology》 2008年第1期141-146,共6页
In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using... In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO. 展开更多
关键词 particle swarm extended particle swarm optimization algorithm resource leveling
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Research on a Distribution Center Location Model Based on a Particle Swarm Optimization Algorithm 认领 引用 被引量：2
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作者 WANG Fei HU Xin-bu JIA Tao 《International Journal of Plant Engineering and Management》 2009年第3期151-157,共7页
Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity.Transportation costs,distribution center construction costs,orderi... Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity.Transportation costs,distribution center construction costs,ordering costs,safe inventory costs and inventory holding costs are the important parts of the total logistics costs.In this paper,based on the research results of LMRP(location model of risk pooling)location with fixed construction cost,the LMRPVCC(location model of risk pooling based on variable construction cost)will be introduced.Applying particle swarm optimization to several computational instances,the authors find the suboptimum solution of the model. 展开更多
关键词 location-inventory particle swarm optimization algorithm variable cost of construction
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Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm 认领 引用
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作者 Rongxin Zhang Chengying Yang Xuetao Li 《Energy Engineering》 EI 2022年第6期2489-2511,共23页
Buildings are the main energy consumers across the world,especially in urban communities.Building smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization too.By control... Buildings are the main energy consumers across the world,especially in urban communities.Building smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization too.By controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be optimized.All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical energy.In this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes.In this way,large power stations that work with fossil fuels will no longer be developed.The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar panels.Additionally,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work.Different case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar panels.The proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for optimization.Scenario generation and reduction were performed via LHS and backward methods,respectively.Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV system.Based on the results,the proposed model markedly reduced the electricity costs of the smart house. 展开更多
关键词 Energy management smart house particle swarm optimization algorithm gravitational search algorithm demand response electric vehicle
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Synergistic Swarm Optimization Algorithm 认领 引用 被引量：5
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作者 Sharaf Alzoubi Laith Abualigah +3 位作者 Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期2557-2604,共48页
This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima... This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:http://gffzz1f8ae908eb0f4369skcwqxbkf59xx6f5o.ffgz.tsg.suse.edu.cn/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm. 展开更多
关键词 Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions
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Shaping the Wavefront of Incident Light with a Strong Robustness Particle Swarm Optimization Algorithm 认领 引用 被引量：4
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作者 李必奇 张彬 +3 位作者 冯祺 程晓明 丁迎春 柳强 《Chinese Physics Letters》 SCIE EI CAS CSCD 2018年第12期15-18,共4页
We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and geneti... We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background. 展开更多
关键词 PSO In Shaping the Wavefront of Incident Light with a Strong Robustness Particle Swarm Optimization Algorithm GA
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Path Planning of Continuum Robot Based on a New Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：5
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作者 Fang Gao Qiang Zhao Gui-Xian Li 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第4期78-84,共7页
Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circu... Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning problem.The algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half stage.This scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and precision.Aiming at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning problem.The detailed solution procedure is presented.Numerical examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient. 展开更多
关键词 continuum robot path planning particle swarm optimization algorithm
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Power optimization of gas pipelines via an improved particle swarm optimization algorithm 认领 引用 被引量：6
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作者 Zheng Zhiwei Wu Changchun 《Petroleum Science》 SCIE CAS CSCD 2012年第1期89-92,共4页
In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization mod... In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming. 展开更多
关键词 Gas pipeline operation optimization particle swarm optimization algorithm
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Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 WU Zhongjian LI Junyan 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第1期59-66,共8页
In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optim... In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optimization challenge.The enormity of the dataset under consideration gives rise to substantial computational burdens,complex modeling,and high hardware requirements.This paper employs the Dual-Population Particle Swarm Optimization(DPSO)algorithm in variational data assimilation to enhance assimilation accuracy.By harnessing parallel computing principles,the paper introduces the Parallel Dual-Population Particle Swarm Optimization(PDPSO)Algorithm to reduce the algorithm processing time.Simulations were carried out using partial differential equations,and comparisons in terms of time and accuracy were made against DPSO,the Dynamic Weight Particle Swarm Algorithm(PSOCIWAC),and the TimeVarying Double Compression Factor Particle Swarm Algorithm(PSOTVCF).Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO.Regarding processing time,PDPSO is 40%faster than PSOCIWAC and PSOTVCF and 70%faster than DPSO. 展开更多
关键词 parallel algorithm variational data assimilation dual-population particle swarm optimization algorithm diffusion mechanism
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Improved Particle Swarm Optimization Algorithm to Solve the Problem of Layout Optimization of Electric Vehicle Charging Stations 认领 引用
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作者 QI Lin YAO Jian WANG Xin-yue 《Journal of Highway and Transportation Research and Development(English Edition)》 2018年第2期96-103,共8页
In charging station services,user charging requirements limit the layout optimization of charging stations to realize total cost minimization.By combining the k-center algorithm and cloud model particle swarm algorith... In charging station services,user charging requirements limit the layout optimization of charging stations to realize total cost minimization.By combining the k-center algorithm and cloud model particle swarm algorithm,this study puts forward a method to improve the global search ability of the adaptive parameter adjustment strategy.A simulation is performed accordingly.Results show that when solving the layout optimization problem for charging stations,the improved adaptive hybrid algorithm that combines the k-center and cloud model particle swarm algorithm outperforms the original cloud model particle swarm algorithm and the basic particle swarm optimization algorithm.The improved algorithm is thus effective. 展开更多
关键词 Traffic engineering layout optimization of electric vehicle charging station improved particle swarm optimization algorithm particle swarm optimization algorithm cloud model Voronoi diagram
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Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 认领 引用 被引量：4
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作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
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Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm 认领 引用
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作者 Yi-Han Wang Hai-Feng Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第4期352-363,共12页
Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded p... Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm. 展开更多
关键词 magnetized plasma photonic crystals improved particle swarm optimization algorithm nonreciprocal ultra-wide band absorption angular insensitivity
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Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 姜文英 林焰 +1 位作者 陈明 于雁云 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第5期565-570,共6页
Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the car... Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach. 展开更多
关键词 cargo oil tank optimization design nonlinear programming improved particle swarm optimization（PSO）algorithm fuzzy constraint construction feasibility degree
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Chicken Swarm Optimization Algorithm Based on Behavior Feedback and Logic Reversal 认领 引用
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作者 Zhenwu Wang Chengfeng Yin 《Journal of Beijing Institute of Technology》 EI CAS 2018年第3期348-356,共9页
Considering the problem that a rooster in chicken swarm optimization(CSO)easily falls into a local optimum and cannot fully demonstrate the population wisdom,the paper proposed an improved CSO algorithm,which based on... Considering the problem that a rooster in chicken swarm optimization(CSO)easily falls into a local optimum and cannot fully demonstrate the population wisdom,the paper proposed an improved CSO algorithm,which based on behavior feedback from hens to rooster and rooster behavior logic reversal,therefore it is named behavior feedback and logic reversal CSO(BFLRCSO).The proposed algorithm changes the original rooster behavior logic to boost the convergence rate,which can accelerate the rooster optimization process,and the algorithm also introduces a feedback mechanism from hens to rooster which can prevent swarm dropping into a local optimum.The experiment results demonstrated that the BFLRCSO algorithm is not easy to fall into a local optimum,which has a better optimization result and shorter optimization time compared with the original CSO algorithm in both high and low dimensional search space. 展开更多
关键词 chicken swarm optimization(CSO)algorithm population wisdom behavior feedback behavior logic reversal
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Gridless Net Routing of Integrate Circuit with Particle Swarm Optimization Algorithm 认领 引用
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作者 X.C. Huang 《Journal of Energy and Power Engineering》 CAS 2011年第9期899-904,共6页
Particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. Particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is success... Particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. Particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is successfully applied in function optimization, neural network design, classification, pattern recognition, signal processing and robot technology and so on. A modified algorithm is presented and applied to the layout of IC design. For a given layout plane, first of all, this algorithm generates the corresponding grid group by barriers and nets＇ ports with the thought ofgridless net routing, establishes initialization fuzzy matrix, then utilizes the global optimization character to find out the best layout route only if it exits. The results of model simulation indicate that PSO algorithm is feasible and efficient in IC layout design. 展开更多
关键词 Particle swarm optimization algorithm gridless net routing layout optimization prufer number.
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