Reconstruction Optimization Design with Incomplete Projection Condition Based on Search Space Pruning and Multi-population Dynamic Adjustment Strategy

The paper deals with the design of refined reconstruction optimization with incomplete projection condition within working face. A method combining search space pruning and multi-population dynamic adjustment is proposed. In this optimization strategy, firstly considering the prior knowledge of the working face reconstruction model structure to reduce the search space, and the observation area is adaptively meshed with different scales. After pruning the search space, the reconstruction objective function is formed. Then, taking into account the needs for population diversity and rapid convergence of multipopulation genetic algorithms, a dynamic multi-population optimization model targeting the reconstruction objective function is proposed, aiming for a fast and accurate acquisition of the optimal solution for the working face reconstruction objective function. In this paper, numerical simulation cases are presented to prove the efficiency of the method proposed.