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.
Highlights
In order to deal with the problems above, this paper proposes an improved genetic algorithm based on improved initial population and intra-group head-tohead mutation operator (IHGA) to cope with the path planning of system to recovery resilience
The variance of fitness is better than that of basic genetic algorithm (BGA) and multi-chromosome genetic algorithm (MCGA). This indicates that the stability, convergence and optimal solution of IHGA proposed are better than BGA and MCGA, especially in the harsh conditions of shorter repair time, more and unbalanced demands for spare parts
This paper proposes an improved genetic algorithm (IHGA) to solve the problem in recovery path planning of system resilience, which optimizes initial population on greedy idea and designs intra-group head-to-head mutation operator (IHMO)
Summary
The term of resilience is originated from early social ecology, which is defined as the ability of an entity to return normal state when being subjected to an event that has changed its state in general semantics. Similar to the concepts of reliability, survivability, and tolerance, resilience is described as a performance indicator of the ability of a system to respond to changes and reduce risks. In order to deal with the problems above, this paper proposes an improved genetic algorithm based on improved initial population and intra-group head-tohead mutation operator (IHGA) to cope with the path planning of system to recovery resilience. This paper makes major innovations and contributions as follows: [1] A planning method is designed to search for optimal path that maximizes system resilience under time, spare parts constraint, and multi-personnel maintenance. This paper uses overall task importance of system as a measurement of system performance, the mathematical model of recovery path planning to system resilience is as follows. Hik represents the time required for the k-th maintenance personnel to repair the i-th node It can be seen from equation [2] that the solution to system resilience recovery is the maintenance path which makes f maximum. Case 3 simulates the system is more damaged and in an unbalanced need of more spare parts
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