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A bi-population immune algorithm for weapon transportation support scheduling problem with pickup and delivery on aircraft carrier deck 认领 引用 被引量：13
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作者 Fang Guo Wei Han +2 位作者 Xi-chao Su Yu-jie Liu Rong-wei Cui 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2023年第4期119-134,共16页
The weapon transportation support scheduling problem on aircraft carrier deck is the key to restricting the sortie rate and combat capability of carrier-based aircraft.This paper studies the problem and presents a nov... The weapon transportation support scheduling problem on aircraft carrier deck is the key to restricting the sortie rate and combat capability of carrier-based aircraft.This paper studies the problem and presents a novel solution architecture.Taking the interference of the carrier-based aircraft deck layout on the weapon transportation route and precedence constraint into consideration,a mixed integer formulation is established to minimize the total objective,which is constituted of makespan,load variance and accumulative transfer time of support unit.Solution approach is developed for the model.Firstly,based on modeling the carrier aircraft parked on deck as convex obstacles,the path library of weapon transportation is constructed through visibility graph and Warshall-Floyd methods.We then propose a bi-population immune algorithm in which a population-based forward/backward scheduling technique,local search schemes and a chaotic catastrophe operator are embedded.Besides,the randomkey solution representation and serial scheduling generation scheme are adopted to conveniently obtain a better solution.The Taguchi method is additionally employed to determine key parameters of the algorithm.Finally,on a set of generated realistic instances,we demonstrate that the proposed algorithm outperforms all compared algorithms designed for similar optimization problems and can significantly improve the efficiency,and that the established model and the bi-population immune algorithm can effectively respond to the weapon support requirements of carrier-based aircraft under different sortie missions. 展开更多
关键词 Carrier-based aircraft Weapon transportation support scheduling Pickup and delivery Bi-population immune algorithm
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Self-adaptive learning based immune algorithm 认领 引用 被引量：6
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作者 许斌 庄毅 +1 位作者 薛羽 王洲 《Journal of Central South University》 SCIE EI CAS 2012年第4期1021-1031,共11页
A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad... A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average. 展开更多
关键词 immune algorithm multi-modal optimization evolutionary computation immtme secondary response self-adaptivelearning
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A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm 认领 引用 被引量：6
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作者 Junfei Qiao Fei Li +2 位作者 Cuili Yang Wenjing Li Ke Gu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第1期276-291,共16页
Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a dis... Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors. 展开更多
关键词 Distance concentration immune algorithm(DCIA) information processing strength(IPS) radial basis function neural network(RBFNN)
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Learning Bayesian network structure with immune algorithm 认领 引用 被引量：6
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作者 Zhiqiang Cai Shubin Si +1 位作者 Shudong Sun Hongyan Dui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2015年第2期282-291,共10页
Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorith... Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently. 展开更多
关键词 structure learning Bayesian network immune algorithm local optimal structure vaccination
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Application of Immune Algorithm to Evaluation of Soil Resource Quality 认领 引用 被引量：3
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作者 YANG Hai-Dong HU Yue-Ming +2 位作者 DENG Fei-Qi CHEN Fei-Xiang WANG Fei 《Pedosphere》 SCIE CAS 2005年第5期660-668,共9页
Based on the geographic information system (GIS) technology, ArcInfo software was adopted to collect, process andanalyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analy... Based on the geographic information system (GIS) technology, ArcInfo software was adopted to collect, process andanalyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analysis method wasused in combining evaluation factors of Guangdong soil resource quality to determine the evaluation units. Because ofits favorable convergent speed and its ability to search solutions, the immune algorithm was applied to the soil resourcequality evaluation model. At the same time, the evaluation results of this newly proposed method were compared to twoother methods: sum of index and fuzzy synthetic. The results indicated that the immune algorithm reflected the actualcondition of soil resource quality more exactly. 展开更多
关键词 evaluation GIS immune algorithm soil resources
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Application of an immune algorithm to settlement prediction 认领 引用 被引量：5
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作者 Jia GUO Jun-jie ZHENG Yong LIU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第1期93-100,共8页
The settlement curve of the foundation endured the ramp load is an S-type curve,which is usually simulated via Poisson curve.Aimed at the difficulty of preferences in Poisson curve,an immune algorithm (IA) is used.IA ... The settlement curve of the foundation endured the ramp load is an S-type curve,which is usually simulated via Poisson curve.Aimed at the difficulty of preferences in Poisson curve,an immune algorithm (IA) is used.IA is able to obtain a multiple quasi-optimum solution while maintaining the population diversity.In this paper,IA is used in an attempt to obtain accurate settlement prediction.The predicted settlements obtained by IA are compared with those predicted by the least squares fitting method (LSM),the Asaoka method and the genetic algorithm (GA).The results show that IA is a useful technique for predicting the settlement of foundations with an acceptable degree of accuracy and has much better performance than GA and the Asaoka methods. 展开更多
关键词 Settlement Prediction Foundation Immune algorithm （IA）
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Fuzzy least squares support vector machine soft measurement model based on adaptive mutative scale chaos immune algorithm 认领 引用 被引量：9
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作者 王涛生 左红艳 《Journal of Central South University》 SCIE EI CAS 2014年第2期593-599,共7页
In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong cou... In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine(FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune algorithm.The simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and robustness.And application results show that the relative errors of the soft measurement model are less than 3.34%. 展开更多
关键词 chaos immune algorithm fuzzy support vector machine
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Optimizing neural network forecast by immune algorithm 认领 引用 被引量：2
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作者 杨淑霞 李翔 +1 位作者 李宁 杨尚东 《Journal of Central South University of Technology》 2006年第5期573-576,共4页
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the dat... Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast. 展开更多
关键词 neural network forecast immune algorithm optimization
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Immune Algorithm for Selecting Optimum Services in Web Services Composition 认领 引用 被引量：4
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作者 GAO Yan NA Jun ZHANG Bin YANG Lei GONG Qiang DAI Yu 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第1期221-225,共5页
For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We ... For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection. 展开更多
关键词 Web services composition optimum selection Immune Algorithm
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Optimizing control of coal flotation by neuro-immune algorithm 认领 引用 被引量：5
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作者 Yang Xiaoping Chris Aldrich 《International Journal of Mining Science and Technology》 EI CAS 2013年第3期407-413,共7页
Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online d... Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles. 展开更多
关键词 Optimizing control Neuro-immune algorithm Neural networks Immune system Coal flotation
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A RBF Network Learning Scheme Using Immune Algorithm Based on Information Entropy 认领 引用
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作者 宫新保 臧小刚 周希朗 《Journal of Donghua University(English Edition)》 EI CAS 2005年第1期37-40,共4页
A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the str... A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances. 展开更多
关键词 radial basis function networks immune algorithm least square method.
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ARTIFICIAL IMMUNE ALGORITHM OF MULTICELLULAR GROUP AND ITS CONVERGENCE 认领 引用
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作者 罗印升 李人厚 张维玺 《Journal of Pharmaceutical Analysis》 CAS 2005年第2期23-27,共5页
Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a ... Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group's artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed. 展开更多
关键词 germinal center reaction B cell artificial immune algorithm multi-modal function
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An Improved Artificial Immune Algorithm with a Dynamic Threshold 认领 引用 被引量：5
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作者 Zhang Qiao Xu Xu Liang Yan-chun 《Journal of Bionic Engineering》 EI 2006年第2期93-97,共5页
An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antib... An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence. 展开更多
关键词 dynamic threshold artificial immune algorithm genetic algorithm antibody
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Immune algorithm for discretization of decision systems in rough set theory 认领 引用 被引量：4
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作者 JIA Ping DAI Jian-hua CHEN Wei-dong PAN Yun-he ZHU Miao-liang 《Journal of Zhejiang University-SCIENCE A》 EI CAS 2006年第4期602-606,共5页
Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough se... Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pre- treating step for other symbolic knowledge discovery or machine learning methods other than rough set theory. 展开更多
关键词 Rough sets Discretization Immune algorithm Decision system
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A fuzzy immune algorithm and its application in solvent tower soft sensor modeling 认领 引用
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作者 孟科 董朝阳 +2 位作者 高晓丹 王海明 李晓 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2015年第2期197-204,共8页
An improved immune algorithm is proposed in this paper. The problems, such as convergence speed and optimization precision, existing in the basic immune algorithm are well addressed. Besides, a fuzzy adaptive method i... An improved immune algorithm is proposed in this paper. The problems, such as convergence speed and optimization precision, existing in the basic immune algorithm are well addressed. Besides, a fuzzy adaptive method is presented by using the fuzzy system to realize the adaptive selection of two key parameters （possibility of crossover and mutation）. By comparing and analyzing the results of several benchmark functions, the performance of fuzzy immune algorithm （FIA） is approved. Not only the difficulty of parameters selection is relieved, but also the precision and stability are improved. At last, the FIA is ap- plied to optimization of the structure and parameters in radial basis function neural network （RBFNN） based on an orthogonal sequential method. And the availability of algorithm is proved by applying RBFNN in modeling in soft sensor of solvent tower. 展开更多
关键词 immune algorithm fuzzy system radial basis function neural network （RBFNN） soft sensor
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Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect 认领 引用 被引量：3
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作者 Baichao CHEN Liangliang WEI +3 位作者 Yuanzhe ZHU Yongheng ZHONG Jiaxin YUAN Yang LEI 《Journal of Modern Power Systems and Clean Energy》 SCIE EI 2017年第5期820-829,共10页
This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effec... This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the algorithm.In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE39-bus system, and obtains the Pareto optimal solutions,from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method. 展开更多
关键词 FCL optimal allocation Fault current mitigation effect Modified inversion operator Pareto optimal solutions Immune algorithm
An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems 认领 引用 被引量：1
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作者 Wei LI Hao-yu PENG +2 位作者 Wei-hang ZHU De-ren SHENG Jian-hong CHEN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第6期877-889,共13页
This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment （TUC） problem in power plant optimization. The mathematical model of the TUC problem is established by a... This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment （TUC） problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modem power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies, which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm （IA） is used as the initial solution of the tabu search （TS） algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry. 展开更多
关键词 Immune algorithm （IA） Tabu search （TS） Optimization method Unit commitment
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Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm 认领 引用 被引量：1
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作者 Xiaochun Wu Weikang Yang Jianrong Cao 《Transportation Safety and Environment》 EI 2023年第4期117-126,共10页
Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is proposed.Considering the effect of noi... Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is proposed.Considering the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring(CSM)system,this study utilizes wavelet threshold denoising to eliminate interference.The results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals.Then in order to attain a lighter weight and shorten the running time of the diagnosis model,Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault diagnosis.Finally,voluminous experiments using veritable power signals collected from CSM are introduced,which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time.This substantiates the validity and feasibility of the presented approach. 展开更多
关键词 railway point machines wavelet threshold denoising Mallat wavelet decomposition artificial immune algorithm
Immune response-based algorithm for optimization of dynamic environments 认领 引用
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作者 史旭华 钱锋 《Journal of Central South University》 SCIE EI CAS 2011年第5期1563-1571,共9页
A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle.The dynamic process of artificial immune response with operators such as immune cloning,mu... A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle.The dynamic process of artificial immune response with operators such as immune cloning,multi-scale variation and gradient-based diversity was modeled.Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens,a sigmoid model that can clearly describe clonal proliferation was proposed.In addition,with the introduction of multiple populations and multi-scale variation,the algorithm can well maintain the population diversity during the dynamic searching process.Unlike traditional artificial immune algorithms,which require randomly generated cells added to the current population to explore its fitness landscape,AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments.Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks.Preliminary experiments show that AIDE can maintain high population diversity during the search process,simultaneously can speed up the optimization.Thus,AIDE is useful for the optimization of dynamic environments. 展开更多
关键词 dynamic optimization artificial immune algorithms immune response multi-scale variation
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Optimization of thread partitioning parameters in speculative multithreading based on artificial immune algorithm 认领 引用 被引量：2
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作者 Yu-xiang LI Yin-liang ZHAO +1 位作者 Bin LIU Shuo JI 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2015年第3期205-216,共12页
Thread partition plays an important role in speculative multithreading （SpMT） for automatic parallelization of ir- regular programs. Using unified values of partition parameters to partition different applications l... Thread partition plays an important role in speculative multithreading （SpMT） for automatic parallelization of ir- regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap- plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold （DT）, lower limit of thread size （TSL）, upper limit of thread size （TSU）, lower limit of spawning distance （SDL）, and upper limit of spawning distance （SDU）. Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com- binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm （AIA） to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench- marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00% on a 4-core platform compared with a machine learning based approach, and 8.92% compared with a heuristics-based approach. 展开更多
关键词 Speculative multithreading Thread partitioning Artificial immune algorithm
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