Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time.In such problems,it is commonly assumed that all problem instances are feasible.In r...Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time.In such problems,it is commonly assumed that all problem instances are feasible.In reality some instances can be infeasible due to various practical issues,such as a sudden change in resource requirements or a big change in the availability of resources.Decision-makers have to determine whether a particular instance is feasible or not,as infeasible instances cannot be solved as there are no solutions to implement.In this case,locating the nearest feasible solution would be valuable information for the decision-makers.In this paper,a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible.To judge the performance of the proposed algorithm,13 well-known dynamic test problems were solved.The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40%over all the environments and it can also find a good,but infeasible solution,when an instance is infeasible.展开更多
Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative mat...Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.展开更多
As the scale of power networks has expanded,the demand for multi-service transmission has gradually increased.The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networ...As the scale of power networks has expanded,the demand for multi-service transmission has gradually increased.The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks.However,it still cannot cope with situations such as wireless access point(AP)failure.To solve this problem,this paper combines orthogonal fre-quency division multiple access(OFDMA)technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails.First,under the premise of AP layout with strong coverage over the whole area,a faulty AP determination method based on beacon frames(BF)is designed.Then,the maximum signal-to-interference ratio(SINR)is used as the principle to select AP reconnection for the affected users.Finally,this paper designs a dynamic access selection model(DASM)for service frames of power Internet of Things(IoTs)and a schedul-ing access optimization model(SAO-MF)based on multi-frame transmission,which enables access optimization for differentiated services.For the above mechanisms,a heuristic resource allocation algorithm is proposed in SAO-MF.Simulation results show that the method can reduce the delay by 15%and improve the throughput by 55%,ensuring high-quality communication in power wireless networks.展开更多
A smooth bidirectional evolutionary structural optimization(SBESO),as a bidirectional version of SESO is proposed to solve the topological optimization of vibrating continuum structures for natural frequencies and dyn...A smooth bidirectional evolutionary structural optimization(SBESO),as a bidirectional version of SESO is proposed to solve the topological optimization of vibrating continuum structures for natural frequencies and dynamic compliance under the transient load.A weighted function is introduced to regulate the mass and stiffness matrix of an element,which has the inefficient element gradually removed from the design domain as if it were undergoing damage.Aiming at maximizing the natural frequency of a structure,the frequency optimization formulation is proposed using the SBESO technique.The effects of various weight functions including constant,linear and sine functions on structural optimization are compared.With the equivalent static load(ESL)method,the dynamic stiffness optimization of a structure is formulated by the SBESO technique.Numerical examples show that compared with the classic BESO method,the SBESO method can efficiently suppress the excessive element deletion by adjusting the element deletion rate and weight function.It is also found that the proposed SBESO technique can obtain an efficient configuration and smooth boundary and demonstrate the advantages over the classic BESO technique.展开更多
The dynamic optimal interpolation(DOI)method is a technique based on quasi-geostrophic dynamics for merging multi-satellite altimeter along-track observations to generate gridded absolute dynamic topography(ADT).Compa...The dynamic optimal interpolation(DOI)method is a technique based on quasi-geostrophic dynamics for merging multi-satellite altimeter along-track observations to generate gridded absolute dynamic topography(ADT).Compared with the linear optimal interpolation(LOI)method,the DOI method can improve the accuracy of gridded ADT locally but with low computational efficiency.Consequently,considering both computational efficiency and accuracy,the DOI method is more suitable to be used only for regional applications.In this study,we propose to evaluate the suitable region for applying the DOI method based on the correlation between the absolute value of the Jacobian operator of the geostrophic stream function and the improvement achieved by the DOI method.After verifying the LOI and DOI methods,the suitable region was investigated in three typical areas:the Gulf Stream(25°N-50°N,55°W-80°W),the Japanese Kuroshio(25°N-45°N,135°E-155°E),and the South China Sea(5°N-25°N,100°E-125°E).We propose to use the DOI method only in regions outside the equatorial region and where the absolute value of the Jacobian operator of the geostrophic stream function is higher than1×10^(-11).展开更多
Purpose–In this work,the authors show the performance of the proposed diploid scheme(a representation where each individual contains two genotypes)with respect to two dynamic optimization problems,while addressing dr...Purpose–In this work,the authors show the performance of the proposed diploid scheme(a representation where each individual contains two genotypes)with respect to two dynamic optimization problems,while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms(EAs)to standard EAs.The paper aims to discuss this issue.Design/methodology/approach–In the proposed diploid representation of EA,each individual possesses two copies of the genotype.In order to convert this pair of genotypes to a single phenotype,each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype.In order to provide a fair and objective comparison,the authors make sure to compare populations which contain the same amount of genetic information,where the only difference is the arrangement and interpretation of the information.The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position.Findings–For small fitness landscapes the haploid(standard)and diploid algorithms perform comparably and are able to find the global optimum very quickly.However,as the search space increases,rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum.Since both algorithms use the same amount of genetic information,it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better.Originality/value–The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele(value)in the vector of values that is the genotype,dominance is adopted across the entire genotype in relation to its homologue.As a result,this representation can be extended across any alphabet,for any optimization function.展开更多
Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the...Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.展开更多
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is pro...Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.展开更多
This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Tho...This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Those exten</span><span><span style="font-family:Verdana;">sions are </span><i><span style="font-family:Verdana;">Negative</span></i> <i><span style="font-family:Verdana;">Gravity</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;">, and </span><i><span style="font-family:Verdana;">Dynamic</span></i> <i><span style="font-family:Verdana;">Threshold</span></i> <i><span style="font-family:Verdana;">Optimization</span></i><span style="font-family:Verdana;">. T</span></span><span style="font-family:Verdana;">he basic CFO heuristic does not include any of these, but adding them substan</span><span style="font-family:Verdana;">tially improves the algorithm’s performance. This paper extends the work r</span><span style="font-family:Verdana;">eported in a previous paper that considered only negative gravity and which </span><span style="font-family:Verdana;">showed a significant performance improvement over a range of optimized a</span><span style="font-family:Verdana;">rrays. Still better results are obtained by adding to the mix </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">DTO</span></i><span style="font-family:Verdana;">. An overall improvement in best fitness of 19.16% is achieved by doing so. While the work reported here was limited to the design/optimization of 6-</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">element Yagis, the reasonable inference based on these data is that any antenna design/optimization problem, indeed any Global Search and Optimiza</span><span style="font-family:Verdana;">tion problem, antenna or not, utilizing Central Force Optimization as the Gl</span><span style="font-family:Verdana;">obal Search and Optimization engine will benefit by including all three extensions, probably substantially.展开更多
This paper first introduces the technical requirements for autonomous flight, with a brief review of the International Academy of Astronautics(IAA) study group, "autonomous dynamic trajectory optimization control...This paper first introduces the technical requirements for autonomous flight, with a brief review of the International Academy of Astronautics(IAA) study group, "autonomous dynamic trajectory optimization control of launch vehicle". Two research scenarios, ascent rescue and powered descent, are compared from the viewpoint of optimal control. On this basis, the technologies on the autonomous trajectory planning and control under the thrust-drop failures in the ascending phase, and the autonomous guidance method during the powered landing for the recovery of the rockets are discussed respectively. For the ascending problem, the characteristics of different solutions, including the iterative guidance method(IGM)-based residual carrying capacity evaluation, the state-triggered indices(STI), the joint planning with the payload’s performance, and the multiple graded optimization(MGO), are analyzed for comparison. For the landing problem, the challenges such as the feasible region reduction caused by high thrust weight ratio(HTWR) and the disturbance adaptability brought by the limited feasible region, are studied in detail, as well as the onboard planning demonstration flight in China are introduced. Finally, the foundations supporting the above methods are summarized, which play an important role in promoting the flight autonomy.展开更多
Spatial information network(SIN)is a network with high speed and periodicity of node operation.In recent days,China will build a complete asteroid monitoring and warning system and a near-Earth asteroid defense system...Spatial information network(SIN)is a network with high speed and periodicity of node operation.In recent days,China will build a complete asteroid monitoring and warning system and a near-Earth asteroid defense system.This requires launching more low-Earth orbit satellites.In order to adapt to the increase in the number of near-Earth satellites,the dynamic optimization of space informa-tion network topology between satellites will have research significance.Consid-ering the visibility of satellite networking,the connectivity of satellite nodes,and the number of links connected to the whole network,with the goal of minimizing the end-to-end delay between satellite nodes in the network as the optimization goal,a network topology optimization model that meets multiple constraints is constructed,and the model is solved using greedy algorithm and simulated anneal-ing algorithm.In the process of simulated annealing,the networkflow algorithm is innovatively proposed for neighborhood solution.Experiments show that the simulated annealing hybrid neighborhood algorithm is significantly better than the simulated annealing random neighborhood algorithm.展开更多
Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and elec...Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and electricity sector regulation,which are also the major components of the carbon and electricity markets,respectively.In this paper,a joint electricity and carbon market model is proposed to investigate the relationships between electricity price,carbon price,and electricity generation capacity,thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework.The proposed model is a dynamically iterative optimization model consisting of upper-level and lower-level models.The upper-level model optimizes power generation and obtains the electricity price,which drives the lower-level model to update the carbon price and electricity generation capacity.The proposed model is verified using the Northeast Asia power grid.The results show that increasing carbon price will result in increased electricity price,along with further increases in renewable energy generation capacity in the following period.This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources,and hence the carbon price will decline.Moreover,the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation.Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050.It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.展开更多
To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which...To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which can deal with the insufficient population diversity and non-global optimal solution issues.The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module.To improve the population diversity,the dynamic neighborhood strategy is first proposed,which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism.The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space,thus obtaining highquality exemplars.Meanwhile,when the global optimal solution cannot update its fitness value,the shuffling mechanism module is triggered to dynamically change the local neighborhood members.The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood.Then,the global learning based particle update approach is proposed,which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage.Finally,the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO.The simulation results show that the proposed DNGL-PSO has excellent adaptability,optimization efficiency and global optimization capability,while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.展开更多
This paper proposes a dynamic opportunistic preventive maintenance (PM) optimization policy for multi-unit series systems by integrating multi PM techniques. Two PM techniques, periodic PM and sequential PM, are consi...This paper proposes a dynamic opportunistic preventive maintenance (PM) optimization policy for multi-unit series systems by integrating multi PM techniques. Two PM techniques, periodic PM and sequential PM, are considered. Whenever one of the units reaches its reliability threshold, a PM action has to be performed on that unit. At that time the whole system has to be stopped and PM opportunities arise for the other unitsof the system. An optimal PM practice is determined by maximizing the short-term cumulative opportunistic maintenance (OM) cost savings for the whole system. Numerical examples are given to show how this approach works. Finally, a comparison between the proposed PM policy and the other policies is given.展开更多
This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MD...This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.展开更多
Battery energy storage systems(BESS)are instrumental in the transition to a low carbon electrical network with enhanced flexibility,however,the set objective can be accomplished only through suitable scheduling of the...Battery energy storage systems(BESS)are instrumental in the transition to a low carbon electrical network with enhanced flexibility,however,the set objective can be accomplished only through suitable scheduling of their operation.This paper develops a dynamic optimal power flow(DOPF)-based scheduling framework to optimize the day(s)-ahead operation of a grid-scale BESS aiming to mitigate the predicted limits on the renewable energy generation as well as smooth out the network demand to be supplied by conventional generators.In DOPF,all the generating units,including the ones that model the exports and imports of the BESS,across the entire network and the complete time horizon are integrated on to a single network.Subsequently,an AC-OPF is applied to dispatch their power outputs to minimize the total generation cost,while satisfying the power balance equations,and handling the unit and network constraints at each time step coupled with intertemporal constraints associated with the state of charge(SOC).Furthermore,the DOPF developed here entails the frequently applied constant current-constant voltage charging profile,which is represented in the SOC domain.Considering the practical application of a 1 MW BESS on a particular 33 kV network,the scheduling framework is designed to meet the pragmatic requirements of the optimum utilization of the available energy capacity of BESS in each cycle,while completing up to one cycle per day.展开更多
In the standard grey wolf optimizer(GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting per...In the standard grey wolf optimizer(GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer(DGWO1) and the second dynamic grey wolf optimizer(DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.展开更多
Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other...Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other Asia companies.In particular,the rotating seru has been widely implemented,where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing tasks.The rotating seru production problem,which determines the rotating sequence of workers as well as the assembling sequence of jobs,is difficult to solve due to conflicting objectives and dynamic release of customer demands.To solve this problem,we propose a dynamic multiobjective NSGA-II based memetic algorithm.Moreover,to preserve desirable population diversity and improve the searching efficiency,we propose different problem-specific evolutionary strategies.Finally,we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.展开更多
This paper extends the independent point-wise density interpolation to the bimaterial to pology optimization to improve the structural static or dynamic proper ties.In contras t to the conventional elemental density-b...This paper extends the independent point-wise density interpolation to the bimaterial to pology optimization to improve the structural static or dynamic proper ties.In contras t to the conventional elemental density-based topology optimization approaches,this method employs an analysis-mesh-separated material density field discretization model to describe the topology evolution of bi-material structures within the design domain.To be specific,the density design variable points can be freely positioned,independently of the field points used for discretization of the displacement field.By this means,a material interface description of relatively high quality can be achieved,even when unstructured finite element meshes and irregular-shaped elements are used in discretization of the analysis domain.Numerical examples,regarding the minimum static compliance design and the maximum fundamental eigen-frequency design,are presented to demonstrate the validity and applicability of the proposed formulation and numerical techniques.It is shown that this method is free of numerical difficulties such as checkerboard patterns and the“islanding”phenomenon.展开更多
基金supported by the Australian Research Council Discovery Project(Grant Nos.DP210102939).
文摘Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time.In such problems,it is commonly assumed that all problem instances are feasible.In reality some instances can be infeasible due to various practical issues,such as a sudden change in resource requirements or a big change in the availability of resources.Decision-makers have to determine whether a particular instance is feasible or not,as infeasible instances cannot be solved as there are no solutions to implement.In this case,locating the nearest feasible solution would be valuable information for the decision-makers.In this paper,a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible.To judge the performance of the proposed algorithm,13 well-known dynamic test problems were solved.The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40%over all the environments and it can also find a good,but infeasible solution,when an instance is infeasible.
文摘Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.
基金supported by State Grid Jiangsu Electric Power Co.,Ltd.Science and Technology Project“Research on Low-Cost Wireless Coverage and Trusted Access Technologies for Underground Pipe Gallery Digital Network”(J2021081).
文摘As the scale of power networks has expanded,the demand for multi-service transmission has gradually increased.The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks.However,it still cannot cope with situations such as wireless access point(AP)failure.To solve this problem,this paper combines orthogonal fre-quency division multiple access(OFDMA)technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails.First,under the premise of AP layout with strong coverage over the whole area,a faulty AP determination method based on beacon frames(BF)is designed.Then,the maximum signal-to-interference ratio(SINR)is used as the principle to select AP reconnection for the affected users.Finally,this paper designs a dynamic access selection model(DASM)for service frames of power Internet of Things(IoTs)and a schedul-ing access optimization model(SAO-MF)based on multi-frame transmission,which enables access optimization for differentiated services.For the above mechanisms,a heuristic resource allocation algorithm is proposed in SAO-MF.Simulation results show that the method can reduce the delay by 15%and improve the throughput by 55%,ensuring high-quality communication in power wireless networks.
基金supported by the National Natural Science Foundation of China (Grant No.51505096)the Natural Science Foundation of Heilongjiang Province (Grant No.LH2020E064).
文摘A smooth bidirectional evolutionary structural optimization(SBESO),as a bidirectional version of SESO is proposed to solve the topological optimization of vibrating continuum structures for natural frequencies and dynamic compliance under the transient load.A weighted function is introduced to regulate the mass and stiffness matrix of an element,which has the inefficient element gradually removed from the design domain as if it were undergoing damage.Aiming at maximizing the natural frequency of a structure,the frequency optimization formulation is proposed using the SBESO technique.The effects of various weight functions including constant,linear and sine functions on structural optimization are compared.With the equivalent static load(ESL)method,the dynamic stiffness optimization of a structure is formulated by the SBESO technique.Numerical examples show that compared with the classic BESO method,the SBESO method can efficiently suppress the excessive element deletion by adjusting the element deletion rate and weight function.It is also found that the proposed SBESO technique can obtain an efficient configuration and smooth boundary and demonstrate the advantages over the classic BESO technique.
基金supported by National Natural Science Foundation of China under Grants 42192531 and 42192534the Special Fund of Hubei Luojia Laboratory(China)under Grant 220100001the Natural Science Foundation of Hubei Province for Distinguished Young Scholars(China)under Grant 2022CFA090。
文摘The dynamic optimal interpolation(DOI)method is a technique based on quasi-geostrophic dynamics for merging multi-satellite altimeter along-track observations to generate gridded absolute dynamic topography(ADT).Compared with the linear optimal interpolation(LOI)method,the DOI method can improve the accuracy of gridded ADT locally but with low computational efficiency.Consequently,considering both computational efficiency and accuracy,the DOI method is more suitable to be used only for regional applications.In this study,we propose to evaluate the suitable region for applying the DOI method based on the correlation between the absolute value of the Jacobian operator of the geostrophic stream function and the improvement achieved by the DOI method.After verifying the LOI and DOI methods,the suitable region was investigated in three typical areas:the Gulf Stream(25°N-50°N,55°W-80°W),the Japanese Kuroshio(25°N-45°N,135°E-155°E),and the South China Sea(5°N-25°N,100°E-125°E).We propose to use the DOI method only in regions outside the equatorial region and where the absolute value of the Jacobian operator of the geostrophic stream function is higher than1×10^(-11).
文摘Purpose–In this work,the authors show the performance of the proposed diploid scheme(a representation where each individual contains two genotypes)with respect to two dynamic optimization problems,while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms(EAs)to standard EAs.The paper aims to discuss this issue.Design/methodology/approach–In the proposed diploid representation of EA,each individual possesses two copies of the genotype.In order to convert this pair of genotypes to a single phenotype,each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype.In order to provide a fair and objective comparison,the authors make sure to compare populations which contain the same amount of genetic information,where the only difference is the arrangement and interpretation of the information.The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position.Findings–For small fitness landscapes the haploid(standard)and diploid algorithms perform comparably and are able to find the global optimum very quickly.However,as the search space increases,rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum.Since both algorithms use the same amount of genetic information,it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better.Originality/value–The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele(value)in the vector of values that is the genotype,dominance is adopted across the entire genotype in relation to its homologue.As a result,this representation can be extended across any alphabet,for any optimization function.
文摘Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.
基金National Natural Science Foundations of China(Nos.61222303,21276078)National High-Tech Research and Development Program of China(No.2012AA040307)+1 种基金New Century Excellent Researcher Award Program from Ministry of Education of China(No.NCET10-0885)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.
文摘This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Those exten</span><span><span style="font-family:Verdana;">sions are </span><i><span style="font-family:Verdana;">Negative</span></i> <i><span style="font-family:Verdana;">Gravity</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;">, and </span><i><span style="font-family:Verdana;">Dynamic</span></i> <i><span style="font-family:Verdana;">Threshold</span></i> <i><span style="font-family:Verdana;">Optimization</span></i><span style="font-family:Verdana;">. T</span></span><span style="font-family:Verdana;">he basic CFO heuristic does not include any of these, but adding them substan</span><span style="font-family:Verdana;">tially improves the algorithm’s performance. This paper extends the work r</span><span style="font-family:Verdana;">eported in a previous paper that considered only negative gravity and which </span><span style="font-family:Verdana;">showed a significant performance improvement over a range of optimized a</span><span style="font-family:Verdana;">rrays. Still better results are obtained by adding to the mix </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">DTO</span></i><span style="font-family:Verdana;">. An overall improvement in best fitness of 19.16% is achieved by doing so. While the work reported here was limited to the design/optimization of 6-</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">element Yagis, the reasonable inference based on these data is that any antenna design/optimization problem, indeed any Global Search and Optimiza</span><span style="font-family:Verdana;">tion problem, antenna or not, utilizing Central Force Optimization as the Gl</span><span style="font-family:Verdana;">obal Search and Optimization engine will benefit by including all three extensions, probably substantially.
文摘This paper first introduces the technical requirements for autonomous flight, with a brief review of the International Academy of Astronautics(IAA) study group, "autonomous dynamic trajectory optimization control of launch vehicle". Two research scenarios, ascent rescue and powered descent, are compared from the viewpoint of optimal control. On this basis, the technologies on the autonomous trajectory planning and control under the thrust-drop failures in the ascending phase, and the autonomous guidance method during the powered landing for the recovery of the rockets are discussed respectively. For the ascending problem, the characteristics of different solutions, including the iterative guidance method(IGM)-based residual carrying capacity evaluation, the state-triggered indices(STI), the joint planning with the payload’s performance, and the multiple graded optimization(MGO), are analyzed for comparison. For the landing problem, the challenges such as the feasible region reduction caused by high thrust weight ratio(HTWR) and the disturbance adaptability brought by the limited feasible region, are studied in detail, as well as the onboard planning demonstration flight in China are introduced. Finally, the foundations supporting the above methods are summarized, which play an important role in promoting the flight autonomy.
基金Supported by Sichuan Science and Technology Program(2023YFG0155).
文摘Spatial information network(SIN)is a network with high speed and periodicity of node operation.In recent days,China will build a complete asteroid monitoring and warning system and a near-Earth asteroid defense system.This requires launching more low-Earth orbit satellites.In order to adapt to the increase in the number of near-Earth satellites,the dynamic optimization of space informa-tion network topology between satellites will have research significance.Consid-ering the visibility of satellite networking,the connectivity of satellite nodes,and the number of links connected to the whole network,with the goal of minimizing the end-to-end delay between satellite nodes in the network as the optimization goal,a network topology optimization model that meets multiple constraints is constructed,and the model is solved using greedy algorithm and simulated anneal-ing algorithm.In the process of simulated annealing,the networkflow algorithm is innovatively proposed for neighborhood solution.Experiments show that the simulated annealing hybrid neighborhood algorithm is significantly better than the simulated annealing random neighborhood algorithm.
基金supported in part by National Key Research and Development Program of China(2016YFB0901900)the Science and Technology Foundation of GEIDCO(SGGEIG00JYJS1900016)
文摘Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and electricity sector regulation,which are also the major components of the carbon and electricity markets,respectively.In this paper,a joint electricity and carbon market model is proposed to investigate the relationships between electricity price,carbon price,and electricity generation capacity,thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework.The proposed model is a dynamically iterative optimization model consisting of upper-level and lower-level models.The upper-level model optimizes power generation and obtains the electricity price,which drives the lower-level model to update the carbon price and electricity generation capacity.The proposed model is verified using the Northeast Asia power grid.The results show that increasing carbon price will result in increased electricity price,along with further increases in renewable energy generation capacity in the following period.This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources,and hence the carbon price will decline.Moreover,the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation.Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050.It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.
基金supported by the National Natural Science Foundation of China(No.:52177028)Aeronautical Science Foundation of China(No.201907051002)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.YWF21BJJ522)the Major Program of the National Natural Science Foundation of China(No.51890882).
文摘To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which can deal with the insufficient population diversity and non-global optimal solution issues.The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module.To improve the population diversity,the dynamic neighborhood strategy is first proposed,which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism.The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space,thus obtaining highquality exemplars.Meanwhile,when the global optimal solution cannot update its fitness value,the shuffling mechanism module is triggered to dynamically change the local neighborhood members.The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood.Then,the global learning based particle update approach is proposed,which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage.Finally,the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO.The simulation results show that the proposed DNGL-PSO has excellent adaptability,optimization efficiency and global optimization capability,while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.
基金the National Natural Science Foundation of China (Nos. 50905115 and 70771065)the National High Technology Research and Development Program (863) of China (Nos. 2009AA043403,2009AA043000 and 2008042801)the Shanghai Natural Science Foundation (No. 09ZR1414400)
文摘This paper proposes a dynamic opportunistic preventive maintenance (PM) optimization policy for multi-unit series systems by integrating multi PM techniques. Two PM techniques, periodic PM and sequential PM, are considered. Whenever one of the units reaches its reliability threshold, a PM action has to be performed on that unit. At that time the whole system has to be stopped and PM opportunities arise for the other unitsof the system. An optimal PM practice is determined by maximizing the short-term cumulative opportunistic maintenance (OM) cost savings for the whole system. Numerical examples are given to show how this approach works. Finally, a comparison between the proposed PM policy and the other policies is given.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 51277015,51677007 and 51977012.
文摘This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.
文摘Battery energy storage systems(BESS)are instrumental in the transition to a low carbon electrical network with enhanced flexibility,however,the set objective can be accomplished only through suitable scheduling of their operation.This paper develops a dynamic optimal power flow(DOPF)-based scheduling framework to optimize the day(s)-ahead operation of a grid-scale BESS aiming to mitigate the predicted limits on the renewable energy generation as well as smooth out the network demand to be supplied by conventional generators.In DOPF,all the generating units,including the ones that model the exports and imports of the BESS,across the entire network and the complete time horizon are integrated on to a single network.Subsequently,an AC-OPF is applied to dispatch their power outputs to minimize the total generation cost,while satisfying the power balance equations,and handling the unit and network constraints at each time step coupled with intertemporal constraints associated with the state of charge(SOC).Furthermore,the DOPF developed here entails the frequently applied constant current-constant voltage charging profile,which is represented in the SOC domain.Considering the practical application of a 1 MW BESS on a particular 33 kV network,the scheduling framework is designed to meet the pragmatic requirements of the optimum utilization of the available energy capacity of BESS in each cycle,while completing up to one cycle per day.
基金Project supported by the Scientific Research Plan Projects of Shaanxi Education Department (No. 20JK0972)the Natural Science Basic Research Project of Shaanxi Province (No. 2021JM-517)the Educational and Teaching Reform Research Project of Xianyang Normal University (No. 2017Y004)。
文摘In the standard grey wolf optimizer(GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer(DGWO1) and the second dynamic grey wolf optimizer(DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.
基金We thank Professor Wei Jiang and two anonymous reviewers for their suggestions and comments.Feng Liu and Jiafu Tang were supported by the NSFC[grant numbers 71872033,71420107028]Kan Fang was supported by the NSFC[grant number 71701144]+1 种基金Yong Yin was supported by the Omron research fund.Feng Liu was also supported by the 2020 LiaoNing Revitalization Talents Program(XLYC2007061)the Dalian High Level Talents Innovation Support Plan(2019RQ107).
文摘Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other Asia companies.In particular,the rotating seru has been widely implemented,where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing tasks.The rotating seru production problem,which determines the rotating sequence of workers as well as the assembling sequence of jobs,is difficult to solve due to conflicting objectives and dynamic release of customer demands.To solve this problem,we propose a dynamic multiobjective NSGA-II based memetic algorithm.Moreover,to preserve desirable population diversity and improve the searching efficiency,we propose different problem-specific evolutionary strategies.Finally,we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.
基金The financial support of the National Natural Science Foundation of China(11425207,U1508209)is gratefully acknowledged.
文摘This paper extends the independent point-wise density interpolation to the bimaterial to pology optimization to improve the structural static or dynamic proper ties.In contras t to the conventional elemental density-based topology optimization approaches,this method employs an analysis-mesh-separated material density field discretization model to describe the topology evolution of bi-material structures within the design domain.To be specific,the density design variable points can be freely positioned,independently of the field points used for discretization of the displacement field.By this means,a material interface description of relatively high quality can be achieved,even when unstructured finite element meshes and irregular-shaped elements are used in discretization of the analysis domain.Numerical examples,regarding the minimum static compliance design and the maximum fundamental eigen-frequency design,are presented to demonstrate the validity and applicability of the proposed formulation and numerical techniques.It is shown that this method is free of numerical difficulties such as checkerboard patterns and the“islanding”phenomenon.
