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
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
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
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim...The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.展开更多
Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services sele...Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.展开更多
In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ...In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.展开更多
With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by incre...With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.展开更多
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,...Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.展开更多
Hydrogen storage and ice storage are promising,environmentally friendly energy storage technologies.However,there are few investigations on the optimal configuration of hybrid renewable energy systems(HRES)for remote ...Hydrogen storage and ice storage are promising,environmentally friendly energy storage technologies.However,there are few investigations on the optimal configuration of hybrid renewable energy systems(HRES)for remote off-grid areas with localized scenarios.This paper proposes a new optimal configuration of an off-grid PV/wind/hydrogen/cooling system.Given three performance indices for evaluating HRES,i.e.,the levelized cost of energy(LCOE),the loss of power supply possibility(LPSP),and the power curtailment rate(PCR),we use theε-constraint method that formulates LCOE as the objective,while LPSP and PCR serve as constraints.Furthermore,to solve the optimal size of HRES,an improved salp swarm algorithm(ISSA)is proposed.The simulation results show that for an off-grid remote community,the LCOE,LPSP,and PCR of the optimal HRES configuration can achieve$0.31/kWh,5.00%,and 7.23%,respectively.The comparison of different systems illustrates that adding ice storage in the HRES with hydrogen storage will decrease the LCOE by 27.12%.In addition,compared with other heuristic algorithms,such as SSA,ISSA offers the configuration with the minimum LCOE.The hydrogen-ice storage system is economically significant to off-grid areas with cooling load demand,and the proposed ISSA has excellent accuracy.展开更多
Aiming at issues of life loss(LL)and overall energy efficiency(OEE)for battery energy storage system(BESS)in smoothing wind power fluctuations,a dynamic grouping control strategy of BESS for remaining useful life(RUL)...Aiming at issues of life loss(LL)and overall energy efficiency(OEE)for battery energy storage system(BESS)in smoothing wind power fluctuations,a dynamic grouping control strategy of BESS for remaining useful life(RUL)extension and OEE improvement is proposed.First,grid-connected power signals are obtained.Second,a model to optimize capacity allocation for three battery groups(BGs)in BESS is established considering LL and OEE,and it is solved by the designed improved beetle swarm antennae search algorithm.Then,a dynamic grouping method is proposed to dynamically adjust the grouping state of battery units(BUs)during operation to keep good sustainable dispatchability.Then,a double-layer power allocation approach coordinated with multi-principle is designed to reduce LL and improve OEE,and also keeps consistency of state of charge for BUs simultaneously.The upper layer achieves power allocation from BESS into the three BGs and power allocation method for each BG is determined.The lower layer,considering PCS efficiency under different working conditions,finishes power allocation from each BG into BUs inside it.Subsequently,an RUL evaluation model based on the swing door trend algorithm is built to shorten required calculation time.Finally,the proposed control strategy is simulated and results compared with other strategies demonstrate the proposed strategy acquires the longest RUL and highest OEE with smoothing wind power fluctuations effectively,which verifies its correctness and validity.展开更多
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘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.
基金the National Special Fund for Agro-scientific Research in the Public Interest(No.201003024)
文摘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.
文摘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.
基金the National Natural Science Foundation of China(52177074).
文摘The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.
基金Project(70631004)supported by the Key Project of the National Natural Science Foundation of ChinaProject(20080440988)supported by the Postdoctoral Science Foundation of China+1 种基金Project(09JJ4030)supported by the Natural Science Foundation of Hunan Province,ChinaProject supported by the Postdoctoral Science Foundation of Central South University,China
文摘Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.
基金supported by the National Natural Science Foundation of China(61472441)
文摘In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.
文摘With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.
基金supported by the National Natural Science Foundation of China (No.11402288)
文摘Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.
基金supported by National Key R&D Program of China(No.2018YFB1500800)National Natural Science Foundation of China(No.51807134)Science and Technology Project of State Grid Corporation,and State Key Laboratory of Power System and Generation Equipment(SKLD21KM10).
文摘Hydrogen storage and ice storage are promising,environmentally friendly energy storage technologies.However,there are few investigations on the optimal configuration of hybrid renewable energy systems(HRES)for remote off-grid areas with localized scenarios.This paper proposes a new optimal configuration of an off-grid PV/wind/hydrogen/cooling system.Given three performance indices for evaluating HRES,i.e.,the levelized cost of energy(LCOE),the loss of power supply possibility(LPSP),and the power curtailment rate(PCR),we use theε-constraint method that formulates LCOE as the objective,while LPSP and PCR serve as constraints.Furthermore,to solve the optimal size of HRES,an improved salp swarm algorithm(ISSA)is proposed.The simulation results show that for an off-grid remote community,the LCOE,LPSP,and PCR of the optimal HRES configuration can achieve$0.31/kWh,5.00%,and 7.23%,respectively.The comparison of different systems illustrates that adding ice storage in the HRES with hydrogen storage will decrease the LCOE by 27.12%.In addition,compared with other heuristic algorithms,such as SSA,ISSA offers the configuration with the minimum LCOE.The hydrogen-ice storage system is economically significant to off-grid areas with cooling load demand,and the proposed ISSA has excellent accuracy.
基金supported by the National Key Research and Development Program of China(No.2018YFE0122200)the National Natural Science Foundation of China(No.52077078)the Fundamental Research Funds for the Central Universities(No.2020MS090).
文摘Aiming at issues of life loss(LL)and overall energy efficiency(OEE)for battery energy storage system(BESS)in smoothing wind power fluctuations,a dynamic grouping control strategy of BESS for remaining useful life(RUL)extension and OEE improvement is proposed.First,grid-connected power signals are obtained.Second,a model to optimize capacity allocation for three battery groups(BGs)in BESS is established considering LL and OEE,and it is solved by the designed improved beetle swarm antennae search algorithm.Then,a dynamic grouping method is proposed to dynamically adjust the grouping state of battery units(BUs)during operation to keep good sustainable dispatchability.Then,a double-layer power allocation approach coordinated with multi-principle is designed to reduce LL and improve OEE,and also keeps consistency of state of charge for BUs simultaneously.The upper layer achieves power allocation from BESS into the three BGs and power allocation method for each BG is determined.The lower layer,considering PCS efficiency under different working conditions,finishes power allocation from each BG into BUs inside it.Subsequently,an RUL evaluation model based on the swing door trend algorithm is built to shorten required calculation time.Finally,the proposed control strategy is simulated and results compared with other strategies demonstrate the proposed strategy acquires the longest RUL and highest OEE with smoothing wind power fluctuations effectively,which verifies its correctness and validity.
