In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have ei...In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have either high computational complexity or relatively poor performance. Delay scheduling is a constraint optimization problem with non-deterministic polynomial（ NP） hard feathers. In this paper,we proposed an immune algorithm-based suboptimal method to solve the problem. Suitable immune operators have been designed such as encoding,clone,mutation and selection. The simulation results show that the proposed algorithm yields near-optimal performance and operates with much lower computational complexity.展开更多
The purpose of this research is to propose an early restoration for lifeline systems after earthquake disasters. The previous researches show that the optimization of the restoration schedule by using genetic algorith...The purpose of this research is to propose an early restoration for lifeline systems after earthquake disasters. The previous researches show that the optimization of the restoration schedule by using genetic algorithm (GA) is powerful. However, those are not considering the uncertain environment after earthquake disasters. The circumstances of the damage at devastated areas are very changeable due to the aftershock, fire disaster and bad weather. In addition, the restoring works may delay by unexpected accidents. Therefore, it is necessary to obtain the restoration schedule which has robustness, because the actual restoring works could not progress smoothly under the uncertain environment. GA considering uncertainty (GACU) can treat various uncertainties involved, but it is difficult to obtain the robust schedule. In this study, an attempt is made to develop a decision support system of the optimal restoration scheduling by using the improved GACU.展开更多
基金Supported by the National Natural Science Foundation of China(U1504613,U1504602)the Research Foundation for the Doctoral Program of China(2015M582622)
文摘In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have either high computational complexity or relatively poor performance. Delay scheduling is a constraint optimization problem with non-deterministic polynomial（ NP） hard feathers. In this paper,we proposed an immune algorithm-based suboptimal method to solve the problem. Suitable immune operators have been designed such as encoding,clone,mutation and selection. The simulation results show that the proposed algorithm yields near-optimal performance and operates with much lower computational complexity.
文摘The purpose of this research is to propose an early restoration for lifeline systems after earthquake disasters. The previous researches show that the optimization of the restoration schedule by using genetic algorithm (GA) is powerful. However, those are not considering the uncertain environment after earthquake disasters. The circumstances of the damage at devastated areas are very changeable due to the aftershock, fire disaster and bad weather. In addition, the restoring works may delay by unexpected accidents. Therefore, it is necessary to obtain the restoration schedule which has robustness, because the actual restoring works could not progress smoothly under the uncertain environment. GA considering uncertainty (GACU) can treat various uncertainties involved, but it is difficult to obtain the robust schedule. In this study, an attempt is made to develop a decision support system of the optimal restoration scheduling by using the improved GACU.
