Hydraulic transition process optimization method based on constraint sensitive variable dominant two stages

By using a two-stage hydraulic transition process optimization method dominated by constraint-sensitive variables, the dominant constraint variables are identified and optimized, and the search direction is dynamically adjusted, which solves the problems of resource waste and low efficiency in existing methods and achieves more efficient water hammer protection parameter optimization.

CN120706254APending Publication Date: 2025-09-26BEIJING UNIV OF TECH +2
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Patent Information

Application Number
CN202510817204.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-18
Publication Date
2025-09-26

AI Technical Summary

Technical Problem

Existing multi-objective optimization methods ignore the differences between constraints and the differences in the degree of constraint violation affected by decision variables in water hammer protection parameter optimization, resulting in waste of computing resources and low optimization efficiency.

Method used

A two-stage hydraulic transition process optimization method based on constraint-sensitive variables is adopted. The dominant constraint variables are identified through Monte Carlo sampling. Differential evolution and polynomial mutation are combined to dynamically adjust the search direction. The decision variables are optimized by utilizing the individual selection mechanism of population state and the cooperative individual update strategy guided by variable type.

Benefits of technology

The algorithm's ability to solve constrained multi-objective optimization problems is improved, the optimization efficiency and the quality of the solution are enhanced, premature convergence is avoided, and the diversity and adaptability of the population are enhanced.

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Abstract

The invention discloses a hydraulic transition process optimization method based on constraint sensitive variable dominant two stages. According to the method, a hydraulic transition process calculation model is constructed, a constraint sensitive variable dominant two-stage cooperative multi-objective evolutionary algorithm is accessed, and dominant constraint variables are determined by utilizing Monte Carlo sampling. After the algorithm parameters and the archive set are initialized, the iterative optimization strategies of the first stage and the second stage are executed in sequence; in the first stage, global search is emphasized, and a population is guided to approach a constrained Pareto frontier; in the second stage, oriented optimization based on constraint sensitive variable recognition results is focused, a cooperative individual updating strategy guided by variable types is adopted, and the sensitive variable optimization precision is improved. According to the method, influence differences of different decision variables are fully mined, resource processing key constraints are concentrated, the search direction is dynamically adjusted, accurate and efficient optimization is achieved, a powerful guarantee is provided for safe and stable operation of hydraulic engineering facilities, the service life of the hydraulic engineering facilities is effectively prolonged, and the maintenance cost is reduced.
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