There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced se...There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.展开更多
A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the princi...A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.展开更多
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct...In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.展开更多
Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient mi...Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient minimum attribute reduction algorithm is proposed based on the multilevel evolutionary tree with self-adaptive subpopulations.A model of multilevel evolutionary tree with self-adaptive subpopulations is constructed,and interacting attribute sets are better decomposed into subsets by the self-adaptive mechanism of elitist populations.Moreover it can self-adapt the subpopulation sizes according to the historical performance record so that interacting attribute decision variables are captured into the same grouped subpopulation,which will be extended to better performance in both quality of solution and competitive computation complexity for minimum attribute reduction.The conducted experiments show the proposed algorithm is better on both efficiency and accuracy of minimum attribute reduction than some representative algorithms.Finally the proposed algorithm is applied to magnetic resonance image(MRI)segmentation,and its stronger applicability is further demonstrated by the effective and robust segmentation results.展开更多
To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The se...To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using som...An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models （i.e., the Lorenz-63 model and the Lorenz-96 model）. Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects： the initial conditions of the Lorenz model, ensemble sizes, observation covarianee, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy- based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.展开更多
Since traditional fault tolerance methods of electronic systems are based on redundant fault tolerance technique,and their structures are fixed when circuits are designed,the self-adaptive ability is limited.In order ...Since traditional fault tolerance methods of electronic systems are based on redundant fault tolerance technique,and their structures are fixed when circuits are designed,the self-adaptive ability is limited.In order to solve these problems,a novel circuit self-adaptive design technique based on evolvable hardware(EHW)is proposed.It features robustness,self-organization and self-adaption.It can be adapted to a complex environment through dynamic configuration of the circuit.In this paper,the proposed technique simulated.The consumption of hardware resources and the number of convergence iterations researched.The effectiveness and superiority of the proposed technique are verified.The designed circuit has the ability of resistible redundant-state interference(RRSI).The proposed technique has a broad application prospect,and it has great significance.展开更多
Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,...Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.展开更多
Two revised drafts about a simple evolution trade off function studied by Mitchell(Mitchell, 2000) were put up first. Considering the complex of the environment, or the nonlinear interaction of the environment and sp...Two revised drafts about a simple evolution trade off function studied by Mitchell(Mitchell, 2000) were put up first. Considering the complex of the environment, or the nonlinear interaction of the environment and species, we put up two new cost functions:c(u,z)=c 0+c 1u+k(z+az 2)u,u>0;c(u,z)=c 0+c 1u+kz du,u>0,d>0. In the first case, if the environment is adverse to species ( a >0), the region of low stress which is more suitable for the intolerant species is very small, and at the same environment stress z , the tolerant species will pay the more cost than it will paid in the normal environment. However the tolerant species will pay more cost but low strategies in the environment of a <0 than that it will paid in the environment of a =0 or a >0. In the second case, the results showed that the greater the stress of the environment is, or the more complex the environment is, the lower cost the intolerant species will pay in the region of z <1. In order to exist or to evolve from an environment of high stress, the organisms must possess a higher u , or a better means of mitigating of the stress of environment. Meanwhile in the region d >1, when d decrease, the intolerant species will pays more lower cost of exploiting a habitat in the low stress environment while the tolerant one will pays more lower cost in the high stress environment. This means that scale d describes the selection character of the species system in the evolution process, the smaller the d(d <1) is, the better the selection or the mitigation the system will possesses.展开更多
Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional m...Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.展开更多
A self-adaptive control method is proposed based on an artificial neural network(ANN)with accelerated evolutionary programming(AEP)algorithm.The neural network is used to model the uncertainty process,from which the t...A self-adaptive control method is proposed based on an artificial neural network(ANN)with accelerated evolutionary programming(AEP)algorithm.The neural network is used to model the uncertainty process,from which the teacher signals are produced online to regulate the parameters of the controller.The accelerated evolutionary programming is used to train the neural network.The experiment results show that the method can obviously improve the dynamic performance of uncertainty systems.展开更多
We study evolutionary games in two-layer networks by introducing the correlation between two layers through the C-dominance or the D-dominance. We assume that individuals play prisoner＇s dilemma game （PDG） in one l...We study evolutionary games in two-layer networks by introducing the correlation between two layers through the C-dominance or the D-dominance. We assume that individuals play prisoner＇s dilemma game （PDG） in one layer and snowdrift game （SDG） in the other. We explore the dependences of the fraction of the strategy cooperation in different layers on the game parameter and initial conditions. The results on two-layer square lattices show that, when cooperation is the dominant strategy, initial conditions strongly influence cooperation in the PDG layer while have no impact in the SDG layer. Moreover, in contrast to the result for PDG in single-layer square lattices, the parameter regime where cooperation could be maintained expands significantly in the PDG layer. We also investigate the effects of mutation and network topology. We find that different mutation rates do not change the cooperation behaviors. Moreover, similar behaviors on cooperation could be found in two-layer random networks.展开更多
We study a spectrum sharing problem where multiple systems coexist and interfere with each other. First, an analysis is proposed for distributed spectrum sharing based on Prisoners' Dilemma (PD) in Cognitive Radio...We study a spectrum sharing problem where multiple systems coexist and interfere with each other. First, an analysis is proposed for distributed spectrum sharing based on Prisoners' Dilemma (PD) in Cognitive Radios (CRs). In one-shot game, selfish and rational CRs greedily full spread their own spectrum space in order to maximize their own rates, which leads to Nash Equilibrium (N.E.). But with long term interaction, i.e., Iterated Prisoner's Dilemma (IPD), CRs can come to cooperate and acquire the social optimal point by using different evolutionary strategies such as Tit For Tat (TFT), Generous TFT (GTFT), etc. Also we compare the performances of the different evolutionary strategies in noise-free and noisy environments for two-player games. Finally, N-player IPD (N-IPD) is simulated to verify our conclusions that TFT is a good strategy for spectrum sharing in CRs.展开更多
We show the practicality of two existing meta-learning algorithms Model-</span></span><span><span><span> </span></span></span><span><span><span><spa...We show the practicality of two existing meta-learning algorithms Model-</span></span><span><span><span> </span></span></span><span><span><span><span style="font-family:Verdana;">Agnostic Meta-Learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propose two novel quantum adaptations of those algorithms using continuous quantum neural networks, for learning to trade portfolios of stocks on the stock market. The goal of meta-learning is to train a model on a variety of tasks, such that it can solve new learning tasks using only a small number of training samples. In our classical approach, we trained our meta-learning models on a variety of portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a pool of 60. In our quantum approach, we trained our </span><span style="font-family:Verdana;">quantum meta-learning models on a simulated quantum computer with</span><span style="font-family:Verdana;"> portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings suggest that both classical models could learn a new portfolio with 0.01% of the number of training samples to learn the original portfolios and can achieve a comparable performance within 0.1% Return on Investment of the Buy and Hold strategy. We also show that our much smaller quantum meta-learned models with only 60 model parameters and 25 training epochs </span><span style="font-family:Verdana;">have a similar learning pattern to our much larger classical meta-learned</span><span style="font-family:Verdana;"> models that have over 250,000 model parameters and 2500 training epochs. Given these findings</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> we also discuss the benefits of scaling up our experiments from a simulated quantum computer to a real quantum computer. To the best of our knowledge, we are the first to apply the ideas of both classical meta-learning as well as quantum meta-learning to enhance stock trading.展开更多
In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become i...In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.展开更多
Characteristics of knowledge exchanging behavior among individual agents in a knowledge dynamic interaction system are studied by using the game theory. An analytic model of evolutionary game of continuous dynamic kno...Characteristics of knowledge exchanging behavior among individual agents in a knowledge dynamic interaction system are studied by using the game theory. An analytic model of evolutionary game of continuous dynamic knowledge interaction behavior is founded based on the structure of the evolutionary game chain. Possible evolution trends of the model are discussed. Finally, evolutionary stable strategies （ESSs） of knowledge transactions among individual agents in the knowledge network are identified by simulation data. Stable charicteristics of ESS in a continuous knowledge exchanging team help employee to communicate and grasp the dynamic regulation of shared knowledge.展开更多
基金supported by the Aviation Science Funds of China(2010ZC13012)the Fund of Jiangsu Innovation Program for Graduate Education (CXLX11 0203)
文摘There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
基金Supported by China Automobile Test Cycle Development Project(CATC2015)
文摘A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.
基金supported by the National Natural Science Foundation of China(Nos.12475174 and 12175101)Yue Lu Shan Center Industrial Innovation(No.2024YCII0108)。
文摘In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.
基金Supported by the National Natural Science Foundation of China(61139002,61171132)the Natural Science Foundation of Jiangsu Education Department(12KJB520013)+2 种基金the Fundamental Research Funds for the Central Universitiesthe Funding of Jiangsu Innovation Program for Graduate Education(CXZZ110219)the Open Project Program of State Key Lab for Novel Software Technology in Nanjing University(KFKT2012B28)
文摘Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient minimum attribute reduction algorithm is proposed based on the multilevel evolutionary tree with self-adaptive subpopulations.A model of multilevel evolutionary tree with self-adaptive subpopulations is constructed,and interacting attribute sets are better decomposed into subsets by the self-adaptive mechanism of elitist populations.Moreover it can self-adapt the subpopulation sizes according to the historical performance record so that interacting attribute decision variables are captured into the same grouped subpopulation,which will be extended to better performance in both quality of solution and competitive computation complexity for minimum attribute reduction.The conducted experiments show the proposed algorithm is better on both efficiency and accuracy of minimum attribute reduction than some representative algorithms.Finally the proposed algorithm is applied to magnetic resonance image(MRI)segmentation,and its stronger applicability is further demonstrated by the effective and robust segmentation results.
文摘To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
基金supported by the NSFC (National Science Foundation of China) project (Grant Nos. 41061038 and 40925004)project "Land Surface Modeling and Data Assimilation Research" (Grant No. 2009AA122104) from the National High Technology ResearchOne Hundred Person Project of the Chinese Academy of Sciences "Multi-sensor Hydrological Data Assimilation for Key Hydrological Variables in Cold and Arid Regions" (Grant No. 29Y127D01)
文摘An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models （i.e., the Lorenz-63 model and the Lorenz-96 model）. Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects： the initial conditions of the Lorenz model, ensemble sizes, observation covarianee, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy- based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.
基金This work was supported by National Natural Science Foundation of China(Nos.61271153 and 61372039).
文摘Since traditional fault tolerance methods of electronic systems are based on redundant fault tolerance technique,and their structures are fixed when circuits are designed,the self-adaptive ability is limited.In order to solve these problems,a novel circuit self-adaptive design technique based on evolvable hardware(EHW)is proposed.It features robustness,self-organization and self-adaption.It can be adapted to a complex environment through dynamic configuration of the circuit.In this paper,the proposed technique simulated.The consumption of hardware resources and the number of convergence iterations researched.The effectiveness and superiority of the proposed technique are verified.The designed circuit has the ability of resistible redundant-state interference(RRSI).The proposed technique has a broad application prospect,and it has great significance.
基金This work was supported by the National Natural Science Foundation of China(62073341)in part by the Natural Science Fund for Distinguished Young Scholars of Hunan Province(2019JJ20026).
文摘Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.
文摘Two revised drafts about a simple evolution trade off function studied by Mitchell(Mitchell, 2000) were put up first. Considering the complex of the environment, or the nonlinear interaction of the environment and species, we put up two new cost functions:c(u,z)=c 0+c 1u+k(z+az 2)u,u>0;c(u,z)=c 0+c 1u+kz du,u>0,d>0. In the first case, if the environment is adverse to species ( a >0), the region of low stress which is more suitable for the intolerant species is very small, and at the same environment stress z , the tolerant species will pay the more cost than it will paid in the normal environment. However the tolerant species will pay more cost but low strategies in the environment of a <0 than that it will paid in the environment of a =0 or a >0. In the second case, the results showed that the greater the stress of the environment is, or the more complex the environment is, the lower cost the intolerant species will pay in the region of z <1. In order to exist or to evolve from an environment of high stress, the organisms must possess a higher u , or a better means of mitigating of the stress of environment. Meanwhile in the region d >1, when d decrease, the intolerant species will pays more lower cost of exploiting a habitat in the low stress environment while the tolerant one will pays more lower cost in the high stress environment. This means that scale d describes the selection character of the species system in the evolution process, the smaller the d(d <1) is, the better the selection or the mitigation the system will possesses.
文摘Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.
基金Key Equipment Project of China Petroleum & Chemical Corporation(SINOPEC)(No.J W05008)
文摘A self-adaptive control method is proposed based on an artificial neural network(ANN)with accelerated evolutionary programming(AEP)algorithm.The neural network is used to model the uncertainty process,from which the teacher signals are produced online to regulate the parameters of the controller.The accelerated evolutionary programming is used to train the neural network.The experiment results show that the method can obviously improve the dynamic performance of uncertainty systems.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11575036,71301012,and 11505016
文摘We study evolutionary games in two-layer networks by introducing the correlation between two layers through the C-dominance or the D-dominance. We assume that individuals play prisoner＇s dilemma game （PDG） in one layer and snowdrift game （SDG） in the other. We explore the dependences of the fraction of the strategy cooperation in different layers on the game parameter and initial conditions. The results on two-layer square lattices show that, when cooperation is the dominant strategy, initial conditions strongly influence cooperation in the PDG layer while have no impact in the SDG layer. Moreover, in contrast to the result for PDG in single-layer square lattices, the parameter regime where cooperation could be maintained expands significantly in the PDG layer. We also investigate the effects of mutation and network topology. We find that different mutation rates do not change the cooperation behaviors. Moreover, similar behaviors on cooperation could be found in two-layer random networks.
基金Supported by the "863" Program (No.2009AA01Z241)the National Natural Science Foundation of China (No.60772062)+2 种基金Key Scientific Research Project of Office of Education in Jiangsu Province (No.06KJA51001)Scientific Research Project of Office of Education in Jiangsu Province (No.8KJB510015)Startup Funding (No.NY208048)
文摘We study a spectrum sharing problem where multiple systems coexist and interfere with each other. First, an analysis is proposed for distributed spectrum sharing based on Prisoners' Dilemma (PD) in Cognitive Radios (CRs). In one-shot game, selfish and rational CRs greedily full spread their own spectrum space in order to maximize their own rates, which leads to Nash Equilibrium (N.E.). But with long term interaction, i.e., Iterated Prisoner's Dilemma (IPD), CRs can come to cooperate and acquire the social optimal point by using different evolutionary strategies such as Tit For Tat (TFT), Generous TFT (GTFT), etc. Also we compare the performances of the different evolutionary strategies in noise-free and noisy environments for two-player games. Finally, N-player IPD (N-IPD) is simulated to verify our conclusions that TFT is a good strategy for spectrum sharing in CRs.
文摘We show the practicality of two existing meta-learning algorithms Model-</span></span><span><span><span> </span></span></span><span><span><span><span style="font-family:Verdana;">Agnostic Meta-Learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propose two novel quantum adaptations of those algorithms using continuous quantum neural networks, for learning to trade portfolios of stocks on the stock market. The goal of meta-learning is to train a model on a variety of tasks, such that it can solve new learning tasks using only a small number of training samples. In our classical approach, we trained our meta-learning models on a variety of portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a pool of 60. In our quantum approach, we trained our </span><span style="font-family:Verdana;">quantum meta-learning models on a simulated quantum computer with</span><span style="font-family:Verdana;"> portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings suggest that both classical models could learn a new portfolio with 0.01% of the number of training samples to learn the original portfolios and can achieve a comparable performance within 0.1% Return on Investment of the Buy and Hold strategy. We also show that our much smaller quantum meta-learned models with only 60 model parameters and 25 training epochs </span><span style="font-family:Verdana;">have a similar learning pattern to our much larger classical meta-learned</span><span style="font-family:Verdana;"> models that have over 250,000 model parameters and 2500 training epochs. Given these findings</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> we also discuss the benefits of scaling up our experiments from a simulated quantum computer to a real quantum computer. To the best of our knowledge, we are the first to apply the ideas of both classical meta-learning as well as quantum meta-learning to enhance stock trading.
基金Sponsored by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2022L294)Taiyuan University of Science and Technology Scientific Research Initial Funding(Grant Nos.W2022018,W20242012)Foundamental Research Program of Shanxi Province(Grant No.202403021212170).
文摘In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.
文摘Characteristics of knowledge exchanging behavior among individual agents in a knowledge dynamic interaction system are studied by using the game theory. An analytic model of evolutionary game of continuous dynamic knowledge interaction behavior is founded based on the structure of the evolutionary game chain. Possible evolution trends of the model are discussed. Finally, evolutionary stable strategies （ESSs） of knowledge transactions among individual agents in the knowledge network are identified by simulation data. Stable charicteristics of ESS in a continuous knowledge exchanging team help employee to communicate and grasp the dynamic regulation of shared knowledge.
