Measurement and control of system resilience recovery by path planning based on improved genetic algorithm

Abstract

Aiming at the problems of basic genetic algorithm in the field of path planning to system resilience recovery such as excessive randomness of initial population, slow convergence, low efficiency of evolution operator, and poor population diversity, this paper uses quotient model to measure resilience, uses overall task importance to measure system performance, and proposes an improved genetic algorithm on initial population and evolutionary operation. Improved genetic algorithm (IHGA) proposes a new greedy model that considers system node tasks importance, travel time, and maintenance time, which uses greedy ideas to generate partial high-quality initial population. And a new operator is also designed as intra-group head-to-head mutation operator (IHMO) to control the evolution to be more determinate and less ineffectively random. The simulation results in three cases show that the IHGA overcomes the defects and can better effectively recover system resilience with comparison to basic genetic algorithm (BGA) and multi-chromosome genetic algorithm (MCGA). Specially, it has obviously better optimal solution, convergence, and stability, especially in the harsh conditions as shorter repair time, more and unbalanced demands for spare parts, which shows the IHGA has great value to deal with measurement and control of system resilience recovery in practice.