Optimal adjustment algorithm for p coordinates is a generalization of the optimal pair adjustment algorithm for linear programming, which in turn is based on von Neumann’s algorithm. Its main advantages are simplicit...Optimal adjustment algorithm for p coordinates is a generalization of the optimal pair adjustment algorithm for linear programming, which in turn is based on von Neumann’s algorithm. Its main advantages are simplicity and quick progress in the early iterations. In this work, to accelerate the convergence of the interior point method, few iterations of this generalized algorithm are applied to the Mehrotra’s heuristic, which determines the starting point for the interior point method in the PCx software. Computational experiments in a set of linear programming problems have shown that this approach reduces the total number of iterations and the running time for many of them, including large-scale ones.展开更多
This research effort addresses the social-distancing problem. As the COVID-19 pandemic continues, we’ve learned the importance of keeping proper distance, so as to avoid (or minimize) the spread of infection. For thi...This research effort addresses the social-distancing problem. As the COVID-19 pandemic continues, we’ve learned the importance of keeping proper distance, so as to avoid (or minimize) the spread of infection. For this paper, individuals are represented as positively-charged particles, behaving in accordance with Coulomb’s Law. Additionally, negatively-charged stationary (non-moving) particles are positioned such that their attraction to the positively-charged particles guides the movement of the positively-charged particles in a desirable fashion. During a simulation process, Coulomb’s Law guides particle behavior such that the positively-charged particles arrange themselves in a way such that their spacing is essentially optimal. Of course, these positively charged particles can be thought of as a surrogate for individuals, resulting in the optimal spacing of individuals.展开更多
Multi-objective genetic algorithm is much suitable for solving multi-objective optimization problems. By use of Genetic algorithm, the optimization of S-boxes is explored in this paper. Results of the experiments show...Multi-objective genetic algorithm is much suitable for solving multi-objective optimization problems. By use of Genetic algorithm, the optimization of S-boxes is explored in this paper. Results of the experiments show that, with heuristic mutation strategy, the algorithm has high searching efficiency and fast convergence speed. Meanwhile, we also have take the avalanche probability of S-boxes into account, besides nonlinearity and difference uniformity. Under this method, an effective genetic algorithm for 6×6 S-boxes is provided and a number of S-boxes with good cryptographic capability can be obtained.展开更多
Reiter presented the first formal framework for model-based diagnosis using logic. However, Reiter’s theory is unimplemented because it suffers from some shortcomings. An extension to Reiter’s diagnostic theory is e...Reiter presented the first formal framework for model-based diagnosis using logic. However, Reiter’s theory is unimplemented because it suffers from some shortcomings. An extension to Reiter’s diagnostic theory is established to overcome the shortcomings. Novel features of such extension include: (i) The fault modes of components are introduced to the behavior description, so that the outputs of both normal and abnormal components can be predicted (ii) Domain-dependent heuristics are used to contract and sort the hypothesis space and assist in making measurements, so that the diagnosis efficiency is improved, (iii) An integrated diagnostic system is proposed based on our theory, and efficient algorithms for computing all diagnoses are developed.展开更多
文摘Optimal adjustment algorithm for p coordinates is a generalization of the optimal pair adjustment algorithm for linear programming, which in turn is based on von Neumann’s algorithm. Its main advantages are simplicity and quick progress in the early iterations. In this work, to accelerate the convergence of the interior point method, few iterations of this generalized algorithm are applied to the Mehrotra’s heuristic, which determines the starting point for the interior point method in the PCx software. Computational experiments in a set of linear programming problems have shown that this approach reduces the total number of iterations and the running time for many of them, including large-scale ones.
文摘This research effort addresses the social-distancing problem. As the COVID-19 pandemic continues, we’ve learned the importance of keeping proper distance, so as to avoid (or minimize) the spread of infection. For this paper, individuals are represented as positively-charged particles, behaving in accordance with Coulomb’s Law. Additionally, negatively-charged stationary (non-moving) particles are positioned such that their attraction to the positively-charged particles guides the movement of the positively-charged particles in a desirable fashion. During a simulation process, Coulomb’s Law guides particle behavior such that the positively-charged particles arrange themselves in a way such that their spacing is essentially optimal. Of course, these positively charged particles can be thought of as a surrogate for individuals, resulting in the optimal spacing of individuals.
基金Supported by the National Natural Science Foundation of China (60473012)
文摘Multi-objective genetic algorithm is much suitable for solving multi-objective optimization problems. By use of Genetic algorithm, the optimization of S-boxes is explored in this paper. Results of the experiments show that, with heuristic mutation strategy, the algorithm has high searching efficiency and fast convergence speed. Meanwhile, we also have take the avalanche probability of S-boxes into account, besides nonlinearity and difference uniformity. Under this method, an effective genetic algorithm for 6×6 S-boxes is provided and a number of S-boxes with good cryptographic capability can be obtained.
基金Project supported by the National Natural Science Foundation of China and Sichuan Youth Science and Technology Foundation
文摘Reiter presented the first formal framework for model-based diagnosis using logic. However, Reiter’s theory is unimplemented because it suffers from some shortcomings. An extension to Reiter’s diagnostic theory is established to overcome the shortcomings. Novel features of such extension include: (i) The fault modes of components are introduced to the behavior description, so that the outputs of both normal and abnormal components can be predicted (ii) Domain-dependent heuristics are used to contract and sort the hypothesis space and assist in making measurements, so that the diagnosis efficiency is improved, (iii) An integrated diagnostic system is proposed based on our theory, and efficient algorithms for computing all diagnoses are developed.
