An Improved Hybrid Genetic Algorithm for the Stochastic Loader Problem

In this paper, we extend Tang´s basic loader problem model by proposing the stochastic quantity of load and unload at each station so that the model is more applicable in practice. For finding the optimal solutions, we present an improved hybrid genetic algorithm that combines self-adapting crossover, learning process and stochastic mutation operators. Comparing with the genetic algorithm, this improved algorithm adequately utilizes the adaptability information of current individuals and has better convergence efficiency and higher solution precision. Two numerical examples illustrate the validity and efficiency of the new hybrid genetic algorithm.