Concerning the derived routes, including Routes 1, 18, 19 and 20, the routes are almost the same as in the case when evacuation distance, arrival probability, and degree of hazard due to fire in the road were optimized; however, a new route, Route 11, which minimizes evacuation time, was derived. Evacuation distance itself is an evaluation value that is determined by distance, and degree of hazard due to fire in the road is an evaluation value obtained by multiplying the degree of hazard due to fire per block/chome by the distance of each road. Due to this, it is thought that the more the evacuation distance for individuals in the population converges on a small value, the more evacuation distance, degree of hazard due to fire in the road, and evacuation time will improve. Route 30 has a relatively long evacuation distance; however, it has a high arrival probability and also a low degree of hazard due to fire. Route 27 has a short evacuation distance, and also, compared to the other derived routes, a relatively high arrival probability.
However, concerning arrival probability, it can be seen that while the average value for arrival probability of individuals within the population improved overall compared to the initial generation with the repeated creation of generations, a clear convergence could not be confirmed, compared with the other three objectives. By using this method, it is possible to find multiple evacuation route candidates whose objectives are in a trade-off relationship with each other in a single search. 3) In the method, road information concerning obstacles to disaster evacuation was created based on data regarding present conditions, such as building and land use data. In this method, searching for evacuation routes based on the information on present conditions allows evacuation routes to be derived based on present building and road locations. Therefore, the number of objectives can be freely increased, and various objectives which disaster evacuation routes are required to meet can be taken into account. The result that is clear from above is that as the number of objectives was increased, multiple routes whose evaluation values for each objective were intrade-off relationships with each other were obtained. In Section 6.2, by considering the change in solutions in the case where the number of objectives is changed, and the convergence of solutions, whether or not multi-objective optimization was conducted appropriately is examined.
When you go to an online store, you need to make it a habit to visit the sales section first. This section describes an evaluation experiment which was carried out in order to verify whether or not multi- objective optimization of evacuation routes was carried out appropriately by the method of this study. In the evaluation experiment in this section, in order to compare derived routes, the same evacuation starting point and the same evacuation destination point were used throughout the experiment. They tend to blow everything on one or two big guests and then the rest seems like filler; compared to a few years ago where major comic artists and big celebrity guests were there the same year. This is because even if the parents were crossed, child individuals which were exactly the same as the parents would be generated. That is, due to the number of types of parent individual increasing, child individuals generated based on those parent individuals also became diverse, and therefore a more global search was possible. This is thought to be due to the fact that because individuals in the population were diverse, the number of types of parent individual in the genetic operations of crossover and mutation increased.
17. 바카라 사이트 , T., Yokota, K. and Sakata, T. (2003) A Stochastic Model Basis for Simulation of Road Blockage Which Takes into Account Individual Collapsed Buildings in Actual Urban Areas in Great Earthquakes. If the London store takes off, others could follow in New York, Brazil and other European cities. Points in each area for which evacuation was considered to be most difficult based on the total degree of hazard (which takes into account the degree of difficulty of conducting activities in times of disaster) were selected as representative evacuation starting points. 1) A GA was used to design and create an evacuation route search algorithm which solves the problem of the optimization of earthquake disaster evacuation routes by treating it as an optimization problem with multiple objectives, such as evacuation distance and evacuation time. Therefore, this route has a shorter evacuation time than Route 27, which has a short route distance. Further, this method is based on publicly available information; therefore, obtaining geographic information similar to that of this study enables this method to be effective regardless of what region it is applied to, or whether the data regards the past or the future. 5. Ichikawa, F., Sakata, T. and Yoshikawa, T. (2001) Study on the Placement of Evacuation Areas, Taking into Account the Danger of Blocked Roads on Evacuation Routes.
4. Takeuchi, T. and Kondou, A. (2002) An Analysis of Evacuation Routes Taking into Consideration Road Sections Blocked by Earthquake-A Case Study of Sazaki City. ► The analysis goes back in time to assess the historical performance of presidential election markets in the US since 1880. ► Using the data, it is possible to evaluate the performance of prediction markets both before and after the introduction of scientific polling. Over time, this fragrance stays soapy, clean, and floral and manages to do that in the most feminine way possible. Further, evacuation time is an evaluation value obtained by using evacuation distance and walking speed to find the regular transit time, and then adding extra time to that due to crowding. Similarly, concerning evacuation time, even if the evacuation time of either of the routes in Case (1) is added to that of any of the derived routes in Table 6, the combined evacuation time will still be within an hour. Concerning the derived routes, the maximum evacuation distance, which belongs to Route 32, is only 1106.7 m.
