In order to reduce the torque ripple,increase the average torque and optimize the drive performance of the switched reluctance motor (SRM),the nonlinear dynamic model of SRM is established in the MATLAB /Simulink envi...In order to reduce the torque ripple,increase the average torque and optimize the drive performance of the switched reluctance motor (SRM),the nonlinear dynamic model of SRM is established in the MATLAB /Simulink environment.The effects of the turn-on and turn-off angles are investigated by the simulation results of the dynamic model,and the function is made among the rotor speed,turn-on angle and turn-off angle.To optimize the torque dynamic performance,the two-objective simultaneous optimization function is proposed by two weight factors.And the optimized turn-on and turn-off angles as functions of rotor speed are developed by using the simultaneous optimization method.Then the optimized torque controller is designed based on the optimized turn-on and turn-off angles.The simulation results show that the optimized torque controller designed in this paper can effectively reduce the torque ripple and increase the average torque,and optimize the torque dynamic performance of the SRM.展开更多
This article attempts to develop a simultaneous optimization procedure of several response variables from incomplete multi-response experiments. In incomplete multi-response experiments all the responses (p) are not r...This article attempts to develop a simultaneous optimization procedure of several response variables from incomplete multi-response experiments. In incomplete multi-response experiments all the responses (p) are not recorded from all the experimental units (n). Two situations of multi-response experiments considered are (i) on units all the responses are recorded while on units a subset of responses is recorded and (ii) on units all the responses (p) are recorded, on units a subset of responses is recorded and on units the remaining subset of responses is recorded. The procedure of estimation of parameters from linear multi-response models for incomplete multi-response experiments has been developed for both the situations. It has been shown that the parameter estimates are consistent and asymptotically unbiased. Using these parameter estimates, simultaneous optimization of incomplete multi-response experiments is attempted following the generalized distance criterion [1]. For the implementation of these procedures, SAS codes have been developed for both complete (k ≤ 5, p = 5) and incomplete (k ≤ 5, p1 = 2, 3 and p2 = 2, 3, where k is the number of factors) multi-response experiments. The procedure developed is illustrated with the help of a real data set.展开更多
A novel methodology is presented for simultaneously optimizing synthesis and cleaning schedule of flexible heat exchanger network(HEN)by genetic/simulated annealing algorithms(GA/SA).Through taking into account the ef...A novel methodology is presented for simultaneously optimizing synthesis and cleaning schedule of flexible heat exchanger network(HEN)by genetic/simulated annealing algorithms(GA/SA).Through taking into account the effect of fouling process on optimal network topology,a preliminary network structure possessing two-fold oversynthesis is obtained by means of pseudo-temperature enthalpy(T-H)diagram approach prior to simultaneous optimization.Thus,the computational complexity of this problem classified as NP(Non-deterministic Polynomial)-complete can be significantly reduced.The promising matches resulting from preliminary synthesis stage are further optimized in parallel with their heat exchange areas and cleaning schedule.In addition,a novel continu- ous time representation is introduced to subdivide the given time horizon into several variable-size intervals according to operating periods of heat exchangers,and then flexible HEN synthesis can be implemented in dynamic manner.A numerical example is provided to demonstrate that the presented strategy is feasible to decrease the total annual cost(TAC)and further improve network flexibility,but even more important,it may be applied to solve large-scale flexible HEN synthesis problems.展开更多
An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are mad...An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.展开更多
Background:Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest.For this,many mathematical ...Background:Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest.For this,many mathematical formulations have been proposed,both within the linear and non-linear programming frameworks,in the latter case generally considering integer variables in a combinatorial manner.We present a novel approach for planning the management of forests comprising single-species,even-aged stands,using a continuous,multi-objective formulation(considering economic and even flow)which can be solved with gradient-type methods.Results:The continuous formulation has proved robust in forest with different structures and different number of stands.The results obtained show a clear advantage of the gradient-type methods over heuristics to solve the problems,both in terms of computational time(efficiency)and in the solution obtained(effectiveness).Their improvement increases drastically with the dimension of the problem(number of stands).Conclusions:It is advisable to rigorously analyze the mathematical properties of the objective functions involved in forest management planning models.The continuous bi-objective model proposed in this paper works with smooth enough functions and can be efficiently solved by using gradient-type techniques.The advantages of the new methodology are summarized as:it does not require to set management prescriptions in advance,it avoids the division of the planning horizon into periods,and it provides better solutions than the traditional combinatorial formulations.Additionally,the graphical display of trade-off information allows an a posteriori articulation of preferences in an intuitive way,therefore being a very interesting tool for the decision-making process in forest planning.展开更多
This article presents a new method to calculate the composition differences(ε)for targeting the mini-mum total annualized cost(TAC)of a mass exchange network(MEN),which is based on the combination of com-position int...This article presents a new method to calculate the composition differences(ε)for targeting the mini-mum total annualized cost(TAC)of a mass exchange network(MEN),which is based on the combination of com-position interval diagram(CID)with mathematical programming.The total cost target consists of the capital cost of the process units and the operating cost for mass separating agents(MSAs).The value of total cost varies consid-erably with the composition differences,so the values of ε should be optimized in order to obtain minimum TAC of a MEN.This article considers ε as a set of unequal variables for each equilibrium equation of a rich-lean stream pair,employing them to build the CID and mathematical model,which optimizes the structure and composition differ-ences simultaneously.Two examples are applied to illustrate the proposed method and the results demonstrate that the approach introduced by this article is simpler and more convenient than the methods in previous literatures.展开更多
The traditional manner to design public transportation system is to sequentially design the transit network and public bicycle network. A new public transportation system design problem that simultaneously considers b...The traditional manner to design public transportation system is to sequentially design the transit network and public bicycle network. A new public transportation system design problem that simultaneously considers both bus network design and public bicycle network design is proposed. The chemical reaction optimization(CRO) is designed to solve the problem. A shortcoming of CRO is that, when the two-molecule collisions take place, the molecules are randomly picked from the container.Hence, we improve CRO by employing different mating strategies. The computational results confirm the benefits of the mating strategies. Numerical experiments are conducted on the Sioux-Falls network. A comparison with the traditional sequential modeling framework indicates that the proposed approach has a better performance and is more robust. The practical applicability of the approach is proved by employing a real size network.展开更多
This paper proposes an individual fitness method genetic algorithm(IFM-GA)for carbon fiber-reinforced plastic(CFRP).The strength of CFRP depends on the carbon fiber allocation and orientation.Waste carbon fiber is gen...This paper proposes an individual fitness method genetic algorithm(IFM-GA)for carbon fiber-reinforced plastic(CFRP).The strength of CFRP depends on the carbon fiber allocation and orientation.Waste carbon fiber is generated if this design is inappropriate.Consequently,CFRPs are less cost-effective.It is necessary to optimize the allocation and orientation as design variables to solve this problem.The problem involves combinatorial optimization.The genetic algorithm(GA)is suitable for combinatorial optimization.However,it is difficult to obtain an optimal solution using the GA owing to the large number of combinations.Hence,the IFM-GA is developed in this study.It is a GA-based method with a different fitness calculation.The GA calculates the fitness of each design,whereas the IFM-GA calculates the fitness of each design element.As a result,the IFM-GA yields a higher-stiffness design than the GA.To conclude,the IFM-GA can enable optimum fiber allocation and orientation,whereas the GA cannot.展开更多
In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strate...In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.展开更多
We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces.The existing structural optimization methods...We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces.The existing structural optimization methods mainly contain shape and topology schemes,with the former changing the surface geometric profile of the structure and the latter changing thematerial distribution topology or hole topology of the structure.In the present acoustic performance optimization,the coordinates of the control points in the subdivision surfaces fine mesh are selected as the shape design parameters of the structure,the artificial density of the sound absorbing material covered on the structure surface is set as the topology design parameter,and the combined topology and shape optimization approach is established through the sound field analysis of the subdivision surfaces boundary element method as a bridge.The topology and shape sensitivities of the approach are calculated using the adjoint variable method,which ensures the efficiency of the optimization.The geometric jaggedness and material distribution discontinuities that appear in the optimization process are overcome to a certain degree by the multiresolution method and solid isotropic material with penalization.Numerical examples are given to validate the effectiveness of the presented optimization approach.展开更多
To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the ...To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.展开更多
Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand...Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.展开更多
In regard to unconventional oil reservoirs,the transient dual-porosity and triple-porosity models have been adopted to describe the fluid flow in the complex fracture network.It has been proven to cause inaccurate pro...In regard to unconventional oil reservoirs,the transient dual-porosity and triple-porosity models have been adopted to describe the fluid flow in the complex fracture network.It has been proven to cause inaccurate production evaluations because of the absence of matrix-macrofracture communication.In addition,most of the existing models are solved analytically based on Laplace transform and numerical inversion.Hence,an approximate analytical solution is derived directly in real-time space considering variable matrix blocks and simultaneous matrix depletion.To simplify the derivation,the simultaneous matrix depletion is divided into two parts:one part feeding the macrofractures and the other part feeding the microfractures.Then,a series of partial differential equations(PDEs)describing the transient flow and boundary conditions are constructed and solved analytically by integration.Finally,a relationship between oil rate and production time in real-time space is obtained.The new model is verified against classical analytical models.When the microfracture system and matrix-macrofracture communication is neglected,the result of the new model agrees with those obtained with the dual-porosity and triple-porosity model,respectively.Certainly,the new model also has an excellent agreement with the numerical model.The model is then applied to two actual tight oil wells completed in western Canada sedimentary basin.After identifying the flow regime,the solution suitably matches the field production data,and the model parameters are determined.Through these output parameters,we can accurately forecast the production and even estimate the petrophysical properties.展开更多
This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This w...This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This work employs 25 different chaotic maps under the framework of Aquila Optimizer.It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be studied in past literature works.It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases.To test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been verified.Finally,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art optimizers.It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.展开更多
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:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.展开更多
Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay ...Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).展开更多
This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encounter...This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.展开更多
Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ...Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.展开更多
Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective ...Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.展开更多
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
基金Sponsored by the Ph.D.Program Foundation of Ministry of Education of China(Grant No.20092302120)
文摘In order to reduce the torque ripple,increase the average torque and optimize the drive performance of the switched reluctance motor (SRM),the nonlinear dynamic model of SRM is established in the MATLAB /Simulink environment.The effects of the turn-on and turn-off angles are investigated by the simulation results of the dynamic model,and the function is made among the rotor speed,turn-on angle and turn-off angle.To optimize the torque dynamic performance,the two-objective simultaneous optimization function is proposed by two weight factors.And the optimized turn-on and turn-off angles as functions of rotor speed are developed by using the simultaneous optimization method.Then the optimized torque controller is designed based on the optimized turn-on and turn-off angles.The simulation results show that the optimized torque controller designed in this paper can effectively reduce the torque ripple and increase the average torque,and optimize the torque dynamic performance of the SRM.
文摘This article attempts to develop a simultaneous optimization procedure of several response variables from incomplete multi-response experiments. In incomplete multi-response experiments all the responses (p) are not recorded from all the experimental units (n). Two situations of multi-response experiments considered are (i) on units all the responses are recorded while on units a subset of responses is recorded and (ii) on units all the responses (p) are recorded, on units a subset of responses is recorded and on units the remaining subset of responses is recorded. The procedure of estimation of parameters from linear multi-response models for incomplete multi-response experiments has been developed for both the situations. It has been shown that the parameter estimates are consistent and asymptotically unbiased. Using these parameter estimates, simultaneous optimization of incomplete multi-response experiments is attempted following the generalized distance criterion [1]. For the implementation of these procedures, SAS codes have been developed for both complete (k ≤ 5, p = 5) and incomplete (k ≤ 5, p1 = 2, 3 and p2 = 2, 3, where k is the number of factors) multi-response experiments. The procedure developed is illustrated with the help of a real data set.
基金Supported by the National Natural Science Foundation of China （20976022） and Dalian University of Technology for Constructing Interdiscipline ＇Energy＋X＇.
ACKNOWLEDGEMENTS The authors gratefully acknowledge financial support from Lanzhou Petrochemical Company, PetroChina Company Limited.
文摘A novel methodology is presented for simultaneously optimizing synthesis and cleaning schedule of flexible heat exchanger network(HEN)by genetic/simulated annealing algorithms(GA/SA).Through taking into account the effect of fouling process on optimal network topology,a preliminary network structure possessing two-fold oversynthesis is obtained by means of pseudo-temperature enthalpy(T-H)diagram approach prior to simultaneous optimization.Thus,the computational complexity of this problem classified as NP(Non-deterministic Polynomial)-complete can be significantly reduced.The promising matches resulting from preliminary synthesis stage are further optimized in parallel with their heat exchange areas and cleaning schedule.In addition,a novel continu- ous time representation is introduced to subdivide the given time horizon into several variable-size intervals according to operating periods of heat exchangers,and then flexible HEN synthesis can be implemented in dynamic manner.A numerical example is provided to demonstrate that the presented strategy is feasible to decrease the total annual cost(TAC)and further improve network flexibility,but even more important,it may be applied to solve large-scale flexible HEN synthesis problems.
基金Project (2002CB312200) supported by the National Key Basic Research and Development Program of China Project(03JJY3109) supported by the Natural Science Foundation of Hunan Province
文摘An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.
文摘Background:Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest.For this,many mathematical formulations have been proposed,both within the linear and non-linear programming frameworks,in the latter case generally considering integer variables in a combinatorial manner.We present a novel approach for planning the management of forests comprising single-species,even-aged stands,using a continuous,multi-objective formulation(considering economic and even flow)which can be solved with gradient-type methods.Results:The continuous formulation has proved robust in forest with different structures and different number of stands.The results obtained show a clear advantage of the gradient-type methods over heuristics to solve the problems,both in terms of computational time(efficiency)and in the solution obtained(effectiveness).Their improvement increases drastically with the dimension of the problem(number of stands).Conclusions:It is advisable to rigorously analyze the mathematical properties of the objective functions involved in forest management planning models.The continuous bi-objective model proposed in this paper works with smooth enough functions and can be efficiently solved by using gradient-type techniques.The advantages of the new methodology are summarized as:it does not require to set management prescriptions in advance,it avoids the division of the planning horizon into periods,and it provides better solutions than the traditional combinatorial formulations.Additionally,the graphical display of trade-off information allows an a posteriori articulation of preferences in an intuitive way,therefore being a very interesting tool for the decision-making process in forest planning.
文摘This article presents a new method to calculate the composition differences(ε)for targeting the mini-mum total annualized cost(TAC)of a mass exchange network(MEN),which is based on the combination of com-position interval diagram(CID)with mathematical programming.The total cost target consists of the capital cost of the process units and the operating cost for mass separating agents(MSAs).The value of total cost varies consid-erably with the composition differences,so the values of ε should be optimized in order to obtain minimum TAC of a MEN.This article considers ε as a set of unequal variables for each equilibrium equation of a rich-lean stream pair,employing them to build the CID and mathematical model,which optimizes the structure and composition differ-ences simultaneously.Two examples are applied to illustrate the proposed method and the results demonstrate that the approach introduced by this article is simpler and more convenient than the methods in previous literatures.
基金Projects(71301115,71271150,71101102)supported by the National Natural Science Foundation of ChinaProject(20130032120009)supported by Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘The traditional manner to design public transportation system is to sequentially design the transit network and public bicycle network. A new public transportation system design problem that simultaneously considers both bus network design and public bicycle network design is proposed. The chemical reaction optimization(CRO) is designed to solve the problem. A shortcoming of CRO is that, when the two-molecule collisions take place, the molecules are randomly picked from the container.Hence, we improve CRO by employing different mating strategies. The computational results confirm the benefits of the mating strategies. Numerical experiments are conducted on the Sioux-Falls network. A comparison with the traditional sequential modeling framework indicates that the proposed approach has a better performance and is more robust. The practical applicability of the approach is proved by employing a real size network.
文摘This paper proposes an individual fitness method genetic algorithm(IFM-GA)for carbon fiber-reinforced plastic(CFRP).The strength of CFRP depends on the carbon fiber allocation and orientation.Waste carbon fiber is generated if this design is inappropriate.Consequently,CFRPs are less cost-effective.It is necessary to optimize the allocation and orientation as design variables to solve this problem.The problem involves combinatorial optimization.The genetic algorithm(GA)is suitable for combinatorial optimization.However,it is difficult to obtain an optimal solution using the GA owing to the large number of combinations.Hence,the IFM-GA is developed in this study.It is a GA-based method with a different fitness calculation.The GA calculates the fitness of each design,whereas the IFM-GA calculates the fitness of each design element.As a result,the IFM-GA yields a higher-stiffness design than the GA.To conclude,the IFM-GA can enable optimum fiber allocation and orientation,whereas the GA cannot.
基金the National Natural Science Foundation of China(Grant No.51305372)the Open Fund Project of the Transportation Infrastructure Intelligent Management and Maintenance Engineering Technology Center of Xiamen City(Grant No.TCIMI201803)the Project of the 2011 Collaborative Innovation Center of Fujian Province(Grant No.2016BJC019).
文摘In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.
基金supported by the National Natural Science Foundation of China (NSFC)under Grant Nos.12172350,11772322 and 11702238。
文摘We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces.The existing structural optimization methods mainly contain shape and topology schemes,with the former changing the surface geometric profile of the structure and the latter changing thematerial distribution topology or hole topology of the structure.In the present acoustic performance optimization,the coordinates of the control points in the subdivision surfaces fine mesh are selected as the shape design parameters of the structure,the artificial density of the sound absorbing material covered on the structure surface is set as the topology design parameter,and the combined topology and shape optimization approach is established through the sound field analysis of the subdivision surfaces boundary element method as a bridge.The topology and shape sensitivities of the approach are calculated using the adjoint variable method,which ensures the efficiency of the optimization.The geometric jaggedness and material distribution discontinuities that appear in the optimization process are overcome to a certain degree by the multiresolution method and solid isotropic material with penalization.Numerical examples are given to validate the effectiveness of the presented optimization approach.
基金supported by the“National Natural Science Foundation of China”(Grant Nos.52105106,52305155)the“Jiangsu Province Natural Science Foundation”(Grant Nos.BK20210342,BK20230904)the“Young Elite Scientists Sponsorship Programby CAST”(Grant No.2023JCJQQT061).
文摘To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.
基金the VNUHCM-University of Information Technology’s Scientific Research Support Fund.
文摘Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.
基金This study was supported by Basic Research Project from Jiangmen Science and Technology Bureau(Grant No.2220002000356)China University of Petroleum(Beijing)(Grand No.2462023BJRC007)The Guangdong Basic and Applied Basic Research Foundation(No.2022A1515110376).
文摘In regard to unconventional oil reservoirs,the transient dual-porosity and triple-porosity models have been adopted to describe the fluid flow in the complex fracture network.It has been proven to cause inaccurate production evaluations because of the absence of matrix-macrofracture communication.In addition,most of the existing models are solved analytically based on Laplace transform and numerical inversion.Hence,an approximate analytical solution is derived directly in real-time space considering variable matrix blocks and simultaneous matrix depletion.To simplify the derivation,the simultaneous matrix depletion is divided into two parts:one part feeding the macrofractures and the other part feeding the microfractures.Then,a series of partial differential equations(PDEs)describing the transient flow and boundary conditions are constructed and solved analytically by integration.Finally,a relationship between oil rate and production time in real-time space is obtained.The new model is verified against classical analytical models.When the microfracture system and matrix-macrofracture communication is neglected,the result of the new model agrees with those obtained with the dual-porosity and triple-porosity model,respectively.Certainly,the new model also has an excellent agreement with the numerical model.The model is then applied to two actual tight oil wells completed in western Canada sedimentary basin.After identifying the flow regime,the solution suitably matches the field production data,and the model parameters are determined.Through these output parameters,we can accurately forecast the production and even estimate the petrophysical properties.
文摘This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This work employs 25 different chaotic maps under the framework of Aquila Optimizer.It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be studied in past literature works.It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases.To test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been verified.Finally,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art optimizers.It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.
基金King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704),King Saud University,Riyadh,Saudi Arabia.
文摘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:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
文摘Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).
基金financially supported by the National Key R&D Program (2022YFB4201302)Guang Dong Basic and Applied Basic Research Foundation (2022A1515240057)the Huaneng Technology Funds (HNKJ20-H88).
文摘This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.
文摘Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208380 and 51979270)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.SKLGME021022).
文摘Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.
基金Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
