Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm opt...Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.展开更多
【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of polic...【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of policy goals, we create a scenario optimization model that minimizes the expected number of fires not receiving a predefined response. In this study, the predefined response indicates the number of firefighting resources that must arrive at a fire before the fire escapes and becomes a large fire. We use spatially explicit GIS-based information on the ecology, fire behavior, and economic characterizations important in Korea. The data include historical fire events in the Republic of Korea from 1991 to 2007, suppression costs, and spatial information on forest fire extent. Interviews with forest managers inform the range of we address in the decision model. Based on the geographic data, we conduct a sensitivity analysis by varying the parameters systematically. Information on the relative importance of the components of the settings helps us to identify “rules of thumb” for initial attack resource allocations in particular ecological and policy settings.展开更多
文摘Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.
文摘【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of policy goals, we create a scenario optimization model that minimizes the expected number of fires not receiving a predefined response. In this study, the predefined response indicates the number of firefighting resources that must arrive at a fire before the fire escapes and becomes a large fire. We use spatially explicit GIS-based information on the ecology, fire behavior, and economic characterizations important in Korea. The data include historical fire events in the Republic of Korea from 1991 to 2007, suppression costs, and spatial information on forest fire extent. Interviews with forest managers inform the range of we address in the decision model. Based on the geographic data, we conduct a sensitivity analysis by varying the parameters systematically. Information on the relative importance of the components of the settings helps us to identify “rules of thumb” for initial attack resource allocations in particular ecological and policy settings.
