Simulation cloning is an efficient way to analyze multiple configurations in a parameter exploration task. A simulation model usually contains a set of tunable parameters for exploring different configurations of a system. To evaluate different design alternatives, multiple simulation instances need to be launched, each evaluating a different parameter configuration. It usually takes a considerable amount of time to execute these simulation instances. Simulation cloning is proposed to reuse computations among simulation instances and to shorten the overall execution time. It is a challenging task to design cloning strategies to explore the computation sharing among simulation instances while maintaining the correctness of execution. In this article, we propose two agent-based simulation (ABS) cloning strategies, the top-down cloning strategy and the bottom-up cloning strategy. The top-down cloning strategy is initially designed and can only be applied to limited scenarios. The bottom-up cloning strategy is an improved strategy to overcome the limitation of the top-down cloning strategy. In the experiments, the effectiveness of the two strategies is analyzed. To show the performance advantages and generality of the bottom-up cloning strategy, a large-scale ABS parameter exploration task is performed, and results are discussed in the article.