Method for optimizing disaster emergency decision system path

Abstract

The invention relates to the technical field of artificial intelligence algorithms, and provides a method for optimizing a disaster emergency decision system path. The method comprises the steps that S11, an objective function and a fitness function are defined; S12, chromosome coding is carried out; S13, operators are selected according to the fitness function; S14, the operators are crossed; S15, the operators are mutated according to the fitness function and pheromone updating guiding mutation rules; S16, a plurality of sets of optimal solutions are generated, and the best solution is output through the fitness function. The quality and efficiency of the solutions are effectively improved through the pheromone guiding mutation rules of an ant colony algorithm. A large amount of useless redundant iteration of a genetic algorithm is avoided through the positive feedback mechanism and the parallelism of the ant colony algorithm, and the solution speed of the genetic algorithm is further improved. The decision model with a single saving point and multiple disaster points based on continuous supply losses is more suitable for emergency situations, and practical significance is achieved.

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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