An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent cros...An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.展开更多
Canonical genetic algorithms have the defects of prematurity and stagnation when applied in optimization problems. The causes resulting in such phenomena were analyzed and a class of improved genetic algorithm with ni...Canonical genetic algorithms have the defects of prematurity and stagnation when applied in optimization problems. The causes resulting in such phenomena were analyzed and a class of improved genetic algorithm with niche implemented by crossover of similar individuals and ( μ+λ ) selection was proposed. According to the reliability design theory of machine components, the genetic optimization model of jack clutch was obtained. An optimization instance and some results calculated by improved genetic algorithm were presented. The results of emulations and application show that the improved genetic algorithm with the niche technique can achieve the reliable global convergence and stable convergent velocity almost without any additional calculation expense. [展开更多
Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequ...Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.展开更多
This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil shapes.The method ensures the integrity of c...This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil shapes.The method ensures the integrity of chiral cell nodal circles while improving load transmission efficiency and enhancing manufacturing precision for 3D printing applications.A parametric design framework,integrating finite element analysis and optimization modules,is developed to enhance the wing’s multidirectional stiffness.The optimization process demonstrates that the distribution of chiral structural ligaments and nodal circles significantly affects wing deformation.The stiffness gradient optimization results reveal a variation of over 78%in tail stiffness performance between the best and worst parameter combinations.Experimental outcomes suggest that this strategy can develop metamaterials with enhanced deformability,offering a promising approach for designing morphing wings.展开更多
Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions var...Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10˚C and an upper temperature of 32˚C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.展开更多
By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite co...By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.展开更多
A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant c...A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.展开更多
The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research project...The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes. However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conven- tional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values： TM=180℃; Pinj = 20MPa; Phold= 16MPa and thold=8s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa. The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant （below 1%）. Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid.展开更多
As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of c...As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.展开更多
The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximi...The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximizing commonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm （2LCGA） is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm （GA） is constructed to vary the amount of platform commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. By incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches （the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage）. The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.展开更多
Although the genetic algorithm （GA） for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the de...Although the genetic algorithm （GA） for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the design process, the authors present an adaptive reanalysis method for GA and its applications in the optimal design of trusses. This reanalysis technique is primarily derived from the Kirsch＇s combined approximations method. An iteration scheme is adopted to adaptively determine the number of basis vectors at every generation. In order to illustrate this method, three classical examples of optimal truss design are used to validate the proposed reanalysis-based design procedure. The presented numerical results demonstrate that the adaptive reanalysis technique affects very slightly the accuracy of the optimal solutions and does accelerate the design process, especially for large-scale structures.展开更多
Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a gen...Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a genetic algorithm （GA） and fuzzy discrete particle swarm optimization （FDPSO） are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical （AIC）, particle swarm optimization （PSO）, and genetic algorithm with particle swarm optimization （GA-PSO）.展开更多
In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which ...In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.展开更多
To increase the robustness of the optimization solutions of the mixed-flow pump,the impeller was firstly indirectly parameterized based on the 2D blade design theory.Secondly,the robustness of the optimization solutio...To increase the robustness of the optimization solutions of the mixed-flow pump,the impeller was firstly indirectly parameterized based on the 2D blade design theory.Secondly,the robustness of the optimization solution was mathematically defined,and then calculated by Monte Carlo sampling method.Thirdly,the optimization on the mixed-flow pump′s impeller was decomposed into the optimal and robust sub-optimization problems,to maximize the pump head and efficiency and minimize the fluctuation degree of them under varying working conditions at the same time.Fourthly,using response surface model,a surrogate model was established between the optimization objectives and control variables of the shape of the impeller.Finally,based on a multi-objective genetic optimization algorithm,a two-loop iterative optimization process was designed to find the optimal solution with good robustness.Comparing the original and optimized pump,it is found that the internal flow field of the optimized pump has been improved under various operating conditions,the hydraulic performance has been improved consequently,and the range of high efficient zone has also been widened.Besides,with the changing of working conditions,the change trend of the hydraulic performance of the optimized pump becomes gentler,the flow field distribution is more uniform,and the influence degree of the varia-tion of working conditions decreases,and the operating stability of the pump is improved.It is concluded that the robust optimization method proposed in this paper is a reasonable way to optimize the mixed-flow pump,and provides references for optimization problems of other fluid machinery.展开更多
A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone con...A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone conformal phased arrays are projected to the tangent plane in one generatrix of the truncated cone. Then two dimensional （2D） Chebyshev amplitude distribution optimization is respectively used in two mutual vertical directions of the tangent plane. According to the location of the elements, the excitation current amplitude distribution of each element on the conformal structure is derived reversely, then the excitation current amplitude is further optimized by using the genetic algorithm （GA）. A truncated cone problem with 8x8 elements on it, and a 3D pattern desired side lobe level （SLL） up to 35 dB, is studied. By using the hybrid method, the optimal goal is accomplished with acceptable CPU time, which indicates that this hybrid method for the low sidelobe synthesis is feasible.展开更多
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip...Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.展开更多
In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response...In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.展开更多
It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optima...It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.展开更多
Aiming at the part quality and building time problems in stereolithography （SL） caused by unreasonable building orientation, a part building orientation decision method in SL rapid prototyping （RP） is carried out....Aiming at the part quality and building time problems in stereolithography （SL） caused by unreasonable building orientation, a part building orientation decision method in SL rapid prototyping （RP） is carried out. Bringing into full consideration of the deformation, stair-stepping effect, overcure effect and building time related to the part fabrication orientation, and using evaluation function method, a multi-objective optimization model for the building orientation is defined. According to the difference in the angles between normal vectors of triangular facets in standard triangulation language （STL） model and z axis, the expressions of deformation area, stair-stepping area, overcure area are established. According to the characteristics in SL process, part building time is divided into four sections, that is, hatching scanning time, outline scanning time, support building time and layer waiting time. Expressions of each building time section are given. Considering the features of this optimization model, genetic algorithm （GA） is used to derive the optimization objective, related software is developed and optimization results are tested through experiments. Application shows that this method can effectively solve the quality and efficiency troubles caused by unreasonable part building orientation, an automatic orientation-determining program is developed and verified through test.展开更多
An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SG...An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm （AGA） based on integer coding and AGA＇s detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.展开更多
文摘An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.
文摘Canonical genetic algorithms have the defects of prematurity and stagnation when applied in optimization problems. The causes resulting in such phenomena were analyzed and a class of improved genetic algorithm with niche implemented by crossover of similar individuals and ( μ+λ ) selection was proposed. According to the reliability design theory of machine components, the genetic optimization model of jack clutch was obtained. An optimization instance and some results calculated by improved genetic algorithm were presented. The results of emulations and application show that the improved genetic algorithm with the niche technique can achieve the reliable global convergence and stable convergent velocity almost without any additional calculation expense. [
文摘Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.
基金Supported by National Natural Science Foundation of China(Grant Nos.52075026 and 52192632)the Fundamental Research Funds for the Central Universities(Grant No.YWF-22-L-1119)。
文摘This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil shapes.The method ensures the integrity of chiral cell nodal circles while improving load transmission efficiency and enhancing manufacturing precision for 3D printing applications.A parametric design framework,integrating finite element analysis and optimization modules,is developed to enhance the wing’s multidirectional stiffness.The optimization process demonstrates that the distribution of chiral structural ligaments and nodal circles significantly affects wing deformation.The stiffness gradient optimization results reveal a variation of over 78%in tail stiffness performance between the best and worst parameter combinations.Experimental outcomes suggest that this strategy can develop metamaterials with enhanced deformability,offering a promising approach for designing morphing wings.
文摘Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10˚C and an upper temperature of 32˚C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.
基金Project(60874114) supported by the National Natural Science Foundation of China
文摘By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.
基金Supported by the National Natural Science Foundation of China (41001195)
文摘A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.
基金Supported by Ministry of Research,Technology and Higher Education of the Republic of Indonesia
文摘The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes. However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conven- tional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values： TM=180℃; Pinj = 20MPa; Phold= 16MPa and thold=8s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa. The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant （below 1%）. Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid.
基金Project(2011ZK2030)supported by the Soft Science Research Plan of Hunan Province,ChinaProject(2010ZDB42)supported by the Social Science Foundation of Hunan Province,China+1 种基金Projects(09A048,11B070)supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProjects(2010GK3036,2011FJ6049)supported by the Science and Technology Plan of Hunan Province,China
文摘As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.
基金This project is supported by National Natural Science Foundation of China(No.70471022,No.70501021)the Joint Research Scheme of National Natural Science Foundation of China(No,70418013) Hong Kong Research Grant Council,China(No.N_HKUST625/04).
文摘The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximizing commonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm （2LCGA） is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm （GA） is constructed to vary the amount of platform commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. By incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches （the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage）. The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.
基金supported by the National Natural Science Foundation of China(50975121)the Project 2009-2007 of the Graduate Innovation Fund of Jilin University
文摘Although the genetic algorithm （GA） for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the design process, the authors present an adaptive reanalysis method for GA and its applications in the optimal design of trusses. This reanalysis technique is primarily derived from the Kirsch＇s combined approximations method. An iteration scheme is adopted to adaptively determine the number of basis vectors at every generation. In order to illustrate this method, three classical examples of optimal truss design are used to validate the proposed reanalysis-based design procedure. The presented numerical results demonstrate that the adaptive reanalysis technique affects very slightly the accuracy of the optimal solutions and does accelerate the design process, especially for large-scale structures.
文摘Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival （DOA）. In this method, a genetic algorithm （GA） and fuzzy discrete particle swarm optimization （FDPSO） are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, a chaotic factor and a crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as the Newton-like algorithm, Akaike information critical （AIC）, particle swarm optimization （PSO）, and genetic algorithm with particle swarm optimization （GA-PSO）.
基金Project of China Postdoctoral Science Foundation,China (No. 2012M510982)Special Fund on the Science and Technology Innovation People of Harbin,China (No. 2011RFQXG002)+2 种基金Technology Item of Heilongjiang Provincial Education Committee,China (No.12511088)Postdoctoral Project of Heilongjiang,China (No. LBH-Z10117 )Youth Fund of Harbin University of Science and Technology,China (No. 2011YF030)
文摘In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.
基金National Natural Science Foundation of China(51609107)Open Subject of Provincial and Ministerial Discipline Platform of Xihua University(szjj2018-123)。
文摘To increase the robustness of the optimization solutions of the mixed-flow pump,the impeller was firstly indirectly parameterized based on the 2D blade design theory.Secondly,the robustness of the optimization solution was mathematically defined,and then calculated by Monte Carlo sampling method.Thirdly,the optimization on the mixed-flow pump′s impeller was decomposed into the optimal and robust sub-optimization problems,to maximize the pump head and efficiency and minimize the fluctuation degree of them under varying working conditions at the same time.Fourthly,using response surface model,a surrogate model was established between the optimization objectives and control variables of the shape of the impeller.Finally,based on a multi-objective genetic optimization algorithm,a two-loop iterative optimization process was designed to find the optimal solution with good robustness.Comparing the original and optimized pump,it is found that the internal flow field of the optimized pump has been improved under various operating conditions,the hydraulic performance has been improved consequently,and the range of high efficient zone has also been widened.Besides,with the changing of working conditions,the change trend of the hydraulic performance of the optimized pump becomes gentler,the flow field distribution is more uniform,and the influence degree of the varia-tion of working conditions decreases,and the operating stability of the pump is improved.It is concluded that the robust optimization method proposed in this paper is a reasonable way to optimize the mixed-flow pump,and provides references for optimization problems of other fluid machinery.
基金supported by the Fundamental Research Funds for the Central Universities(YWF-13D2-XX-13)the National High-tech Research and Development Program(863 Program)(2008AA121802)
文摘A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone conformal phased arrays are projected to the tangent plane in one generatrix of the truncated cone. Then two dimensional （2D） Chebyshev amplitude distribution optimization is respectively used in two mutual vertical directions of the tangent plane. According to the location of the elements, the excitation current amplitude distribution of each element on the conformal structure is derived reversely, then the excitation current amplitude is further optimized by using the genetic algorithm （GA）. A truncated cone problem with 8x8 elements on it, and a 3D pattern desired side lobe level （SLL） up to 35 dB, is studied. By using the hybrid method, the optimal goal is accomplished with acceptable CPU time, which indicates that this hybrid method for the low sidelobe synthesis is feasible.
基金Supported by the National Natural Science Foundation of China(21276078)"Shu Guang"project of Shanghai Municipal Education Commission,973 Program of China(2012CB720500)the Shanghai Science and Technology Program(13QH1401200)
文摘Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
基金Project(20091102110021)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.
基金supported by the National Natural Science Foundation of China(6107901361079014+4 种基金61403198)the National Natural Science Funds and Civil Aviaiton Mutual Funds(U1533128U1233114)the Programs of Natural Science Foundation of China and China Civil Aviation Joint Fund(60939003)the Natural Science Foundation of Jiangsu Province in China(BK2011737)
文摘It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.
基金This project is supported by National Hi-tech Research and Development Program of China(863 Program, No.2005AA414020).
文摘Aiming at the part quality and building time problems in stereolithography （SL） caused by unreasonable building orientation, a part building orientation decision method in SL rapid prototyping （RP） is carried out. Bringing into full consideration of the deformation, stair-stepping effect, overcure effect and building time related to the part fabrication orientation, and using evaluation function method, a multi-objective optimization model for the building orientation is defined. According to the difference in the angles between normal vectors of triangular facets in standard triangulation language （STL） model and z axis, the expressions of deformation area, stair-stepping area, overcure area are established. According to the characteristics in SL process, part building time is divided into four sections, that is, hatching scanning time, outline scanning time, support building time and layer waiting time. Expressions of each building time section are given. Considering the features of this optimization model, genetic algorithm （GA） is used to derive the optimization objective, related software is developed and optimization results are tested through experiments. Application shows that this method can effectively solve the quality and efficiency troubles caused by unreasonable part building orientation, an automatic orientation-determining program is developed and verified through test.
基金This project is supported by National Natural Science Foundation of China (No.50575153)Provincial Key Technology Projects of Sichuan, China (No.03GG010-002)
文摘An improved genetic algorithm and its application to resolve cutting stock problem arc presented. It is common to apply simple genetic algorithm （SGA） to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm （AGA） based on integer coding and AGA＇s detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.
