融合种群遗传和反向学习的路基加固结构参数智能优化OA

[Objective]This study aims to solve the problems that parameter optimization of subgrade reinforcement structures relies on manual experience and has low efficiency,and that a single algorithm has poor universality,and to promote the transformation of subgrade structure design from"empirical trial calculation"to"intelligent optimization".[Methods]A mathematical model for parameter optimization was established,and an intelligent parameter optimization method integrating genetic algorithm and opposition-based learning was proposed.A greedy learning mechanism was adopted to expand the search space and enhance solution diversity.Random factors and mutation disturbances were introduced to improve the crossover and mutation operators,thereby enhancing the local search capability for optimal solutions.A dynamic adaptive operator was further constructed to realize adaptive iteration of gradient and step size,improving the global search capability and convergence speed of the algorithm.Engineering cases of counterfort retaining walls and composite foundations were used for parameter optimization,and the results were compared with those of manual design and the classical genetic algorithm.[Results]The results showed that the intelligent parameter optimization method integrating genetic algorithm and opposition-based learning had significant advantages in both optimization efficiency and quality.The obtained objective function values were closer to the optimum.The optimization time was only 1/12 to 1/15 of that required for manual design and was reduced by 1/4 to 1/3 compared with the classical genetic algorithm.The distribution of individuals generated by the algorithm was more concentrated,indicating a high probability of producing excellent individuals,strong stability,and outstanding global search capability.[Conclusion]The integrated mechanism of genetic algorithm and opposition-based learning compensates for the inherent defects of a single heuristic strategy in the optimization process.It is applicable to parameter optimization of different types of subgrade reinforcement structures,providing important support for the refined and efficient development of subgrade reinforcement structure design.