遗传算法改进及其在岩土参数反分析中的应用

The aim of this paper is to develop a new hybrid real-coded genetic algorithm to identify soil parameters.The new development is under the framework of a classical GA by combining two recently developed and efficient crossover operators with a hybrid strategy.A dynamic random mutation has been incorporated into the new RCGA to maintain the diversity of the population.Additionally,in order to improve the convergence speed,a chaotic local search(CLS) has been adopted.The new GA is applied to identify parameters from an in-situ pressuremeter test and an excavation respectively.In order to highlight the performance of the new GA,5 classic optimization methods(classic genetic algorithm,particle swarm optimization,simulated annealing,differential evolution algorithm and artificial bee colony algorithm) are selected to solve the same problems.The search ability and efficiency of the new hybrid RCGA is estimated by comparisons of all the above methods.