A System Elastic Restoration Method and Restoration System Based on Improved Genetic Algorithm

Abstract

The invention discloses a system elastic restoration method based on an improved genetic algorithm and a restoration system. The restoration method includes the following steps: initializing algorithm parameters, encoding individual populations as maintenance paths of nodes; generating initial populations, and some individuals of the initial populations are random The remaining part is generated according to the greedy strategy; according to the fitness function, evaluate the fitness value of each generation of population individuals, and record the current optimal individual and its fitness value of the population; retain elite individuals for the parent population, through inter-group reversal, crossover and sliding to generate offspring populations; match parent individuals and offspring individuals with special genes to realize head-to-head variation in the group, finally generate offspring populations, and provide a system resilience recovery system based on improved genetic algorithm. The restoration method and restoration system of the present invention are close to the requirements of the actual environment, have fast convergence speed and are not easy to be premature, and can effectively restore system elasticity within a certain period of time.

Description

technical field [0001] The invention relates to a system elastic recovery method and a recovery system, in particular to a system elastic recovery method and a recovery system based on an improved genetic algorithm. Background technique [0002] Resilience, like security, is viewed as an attribute of a system, i.e., the ability of the system to recover its basic (or general) functionality after an attack (or disturbance) that causes physical damage to the system beyond its control. A resilient system can better adapt to rapidly changing, uncertain, and fiercely contested environments. At present, the resilience of the system is mostly based on the corresponding recovery methods after the system is damaged, such as artificial repair. Then, when most components need to be repaired, reasonable maintenance target setting, maintenance resource scheduling and optimal maintenance path planning become the core issues of maintenance strategy. In the short term, the strength of the ...

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Problems solved by technology

The genetic algorithm has the characteristics of being able to find the global optimal solution of the optimization problem, but the initial population of the traditional genetic algorithm is random, and the convergence speed is slow, and it is easy to be premature.

At the same time, in the existing cases of using genetic algorithms to solve system elastic recovery problems, the setting of fitness function and constraint conditions rarely consider task importance, spare parts constraints and time constraints at the same time

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