To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering th...To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.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
基金Supported by National "863" Project of China (2006AA09A104)
文摘To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.
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.
