A method for path optimization of disaster emergency decision-making system

Abstract

The present invention relates to the technical field of artificial intelligence algorithms, and provides a method for path optimization of a disaster emergency decision-making system, including: step S11, defining an objective function and a fitness function; step S12, performing chromosome coding; and step S13, selecting according to the fitness function Operator; step S14, crossover operator; step S15, update the guidance mutation rule mutation operator according to the fitness function and pheromone; step S16, generate several sets of optimal solutions, and output the best solution through the fitness function. The invention effectively improves the quality and efficiency of the solution through the pheromone-guided mutation rules of the ant colony algorithm; utilizes the positive feedback mechanism and parallelism of the ant colony algorithm to avoid a large number of useless redundant iterations of the genetic algorithm, and further improves the solution of the genetic algorithm Speed; the decision-making model based on the continuous loss of materials for a single rescue point and multiple disaster points is more in line with the emergency situation and has practical significance.

Description

【Technical field】 [0001] The invention relates to the technical field of artificial intelligence algorithms, in particular to a method for path optimization of a disaster emergency decision-making system. 【Background technique】 [0002] For thousands of years, natural disasters and man-made accidents have all posed a huge threat to human life and property. Therefore, it is very important to study how to improve the efficiency of emergency material dispatching to reduce casualties and economic losses. With the rapid development of science and technology, in recent years, there have been more and more researches on the route optimization of disaster emergency systems, and uncertain factors have also appeared in the scope of emergency decision-making models, but most of the current research is uncertain The decision-making model of multi-rescue point and single disaster point is uncertain, while there are few studies on the uncertainty of single rescue point, multiple disaster ...

AI Technical Summary

Problems solved by technology

The ant colony algorithm converges on the most effective path through the accumulation and update of pheromones. Although it has distributed parallel global search capabilities, it lacks pheromones at the beginning and the solution speed is very slow.

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