Study on scheme optimization of urban flood disaster prevention and reduction
The decision in flood disaster prevention and reduction will affect the evaluation of flood disaster directly. How to apply the natural, economic and social information, through anticipatory analysis, to ensure the minimum damage after practicing certain optimum scheme is a core problem in the flood disaster prevention and reduction decision-making. In this paper, the factors affecting this decision-making are analyzed, the fuzzy optimum selection and neural network are applied in scheme optimization of urban flood disaster prevention and reduction, and the model of scheme optimization is established.
Analysis of the Disaster Prevention Marking System for Urban Public Building
The denseness of urban public buildings and the complication of the underground space spur the enhancement of disaster prevention and mitigation planning. As a result, for the urban public buildings around the densely populated area, the establishment of public buildings disaster prevention system in urban and the study on some related questions lay the basis of disaster prevention and mitigation. Since the concept of public building have the different definitions in the national standards and different types of buildings require different signs on disaster prevention, the category of public buildings has been redefined according to international normative standards. Furthermore, the research on the types, colors, sizes and position of disaster prevention signs has been done according to the internal, external of building and evacuation routes aspects. Finally, the basic functions of disaster prevention sign system in and around the buildings and the settings of the disaster prevention sign system of different types of public buildings are proposed.
Two-Phase Archive Updating Strategy for Particle Swarm Optimization
Considering the importance of the archive updating strategy for multi-objective particle swarm optimization,a two-phase archive updating strategy was proposed.In the first phase,the adaptive archiving strategy is used to guarantee good distribution and extension of the solutions.In the second phase,the idea of the archive updating strategy based on e-dominance relationship is used to preclude the solutions' deterioration and to guarantee the solutions' convergence.A new selecting strategy of the archive's candidates is also proposed in order to avoid the similar individuals entering in and out the archive frequently.Finally,it is shown from experimental results on complicated benchmark multi-objective functions optimization that the two-phase archive updating strategy and the selecting strategy of the archive's candidates are efficient.