2 results about "Hybrid genetic algorithms" patented technology

This invention discloses a dual-tunnel scheduling optimization method based on a hybrid genetic algorithm and rolling time domain, belonging to the field of tunnel engineering construction management technology. It includes: constructing a multi-objective scheduling model for dual-tunnel construction with the objectives of minimizing the total project duration and minimizing the weighted resource peak value; using a constraint method to transform the multi-objective scheduling model into a resource-balanced single-objective optimization model under a fixed project duration constraint; using a hybrid genetic algorithm to perform static global optimization on the single-objective optimization model; establishing a dynamic monitoring mechanism based on rolling time domain through multi-dimensional IoT sensing; updating the parameters or status of affected processes according to the type of interference event when interference events are detected; using a heuristic local scheduling strategy to reschedule unscheduled tasks after interference, predicting the final project state, and generating a dynamically adjusted construction scheduling scheme. This invention combines static global optimization with dynamic real-time adjustment to achieve dual optimization of project duration and resources.

The application discloses a double-fed fan control identification method based on a hybrid genetic algorithm, and comprises the following steps: step S1, a control model of a double-fed fan RSC is established, and double-fed fan identification parameters are determined according to the control model of the double-fed fan RSC; step S2, an immune algorithm and an adaptability optimization function are introduced to construct a hybrid genetic algorithm, and the hybrid genetic algorithm is used to identify the identification parameters; step S3, an individual identification, an elite reservation and a whole identification strategy are used for iterative training, and trained identification parameters are obtained; and step S4, the trained identification parameters are input into an identification model to obtain corresponding active power and reactive power output simulation curves, a weighted average absolute deviation is calculated according to the output simulation curves, and error analysis is carried out according to the weighted average absolute deviation; the application introduces an immune strategy, and elite parameters in the parameters are reserved in the iteration process, so that the convergence speed is improved, and falling into a local solution is avoided.