A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The f...A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.展开更多
The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA...The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.展开更多
This paper addresses the problem of adaptive,consistent parameter estimation for a MA model from the 3rd order cumulant of the system output. The proposed adaptive algorithm is derived by using the new linear equation...This paper addresses the problem of adaptive,consistent parameter estimation for a MA model from the 3rd order cumulant of the system output. The proposed adaptive algorithm is derived by using the new linear equation system (J. K. Tugnait, 1990), which is proved to have unique solution,and hence guarantees the consistence of the MA parameters. Simulation results are provided to show the performance of the new algorithm.展开更多
Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
Structural analysis problems can be formulized as either root finding problems,or optimization problems.The general practice is to choose the first option directly or to convert the second option again to a root findi...Structural analysis problems can be formulized as either root finding problems,or optimization problems.The general practice is to choose the first option directly or to convert the second option again to a root finding problem by taking relevant derivatives and equating them to zero.The second alternative is used very randomly as it is and only for some simple demonstrative problems,most probably due to difficulty in solving optimization problems by classical methods.The method called TPO/MA(Total Potential Optimization using Metaheuristic Algorithms)described in this study successfully enables to handle structural problems with optimization formulation.Using metaheuristic algorithms provides additional advantages in dealing with all kinds of constraints.展开更多
针对经典贪婪算法(greedy)迭代次数多、运算量大的缺点,提出一种基于边缘自适应(margin adaptive,MA)准则的改进贪婪算法来进行正交频分复用(orthogonal frequency division multiplexing,OFDM)系统的自适应比特功率分配。与贪婪算法相...针对经典贪婪算法(greedy)迭代次数多、运算量大的缺点,提出一种基于边缘自适应(margin adaptive,MA)准则的改进贪婪算法来进行正交频分复用(orthogonal frequency division multiplexing,OFDM)系统的自适应比特功率分配。与贪婪算法相比,改进算法通过预分配和迭代分配两部分来降低算法的计算量。在预分配中改进算法通过引入功率利用率函数,对信道条件好的子信道预先加载一部分比特。然后,在迭代分配的过程中,引用分类排序的思想,用一张表格存储子信道的功率变化情况,从而降低算法的复杂度。仿真结果表明,在相同的仿真环境下,改进算法和Greedy算法的误比特性能几乎一致,但改进算法的运行时间更短。展开更多
基金supported by the National Natural Science Foundation of China(71171038)
文摘A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.
文摘The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.
文摘This paper addresses the problem of adaptive,consistent parameter estimation for a MA model from the 3rd order cumulant of the system output. The proposed adaptive algorithm is derived by using the new linear equation system (J. K. Tugnait, 1990), which is proved to have unique solution,and hence guarantees the consistence of the MA parameters. Simulation results are provided to show the performance of the new algorithm.
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
文摘Structural analysis problems can be formulized as either root finding problems,or optimization problems.The general practice is to choose the first option directly or to convert the second option again to a root finding problem by taking relevant derivatives and equating them to zero.The second alternative is used very randomly as it is and only for some simple demonstrative problems,most probably due to difficulty in solving optimization problems by classical methods.The method called TPO/MA(Total Potential Optimization using Metaheuristic Algorithms)described in this study successfully enables to handle structural problems with optimization formulation.Using metaheuristic algorithms provides additional advantages in dealing with all kinds of constraints.
文摘针对经典贪婪算法(greedy)迭代次数多、运算量大的缺点,提出一种基于边缘自适应(margin adaptive,MA)准则的改进贪婪算法来进行正交频分复用(orthogonal frequency division multiplexing,OFDM)系统的自适应比特功率分配。与贪婪算法相比,改进算法通过预分配和迭代分配两部分来降低算法的计算量。在预分配中改进算法通过引入功率利用率函数,对信道条件好的子信道预先加载一部分比特。然后,在迭代分配的过程中,引用分类排序的思想,用一张表格存储子信道的功率变化情况,从而降低算法的复杂度。仿真结果表明,在相同的仿真环境下,改进算法和Greedy算法的误比特性能几乎一致,但改进算法的运行时间更短。
