In order to increase the probability of population-based search approach to capture specific objects from the decision space within limited time, based on the classical probability model, this paper established an overall random sampling model and a partition sampling model inspired by stratified sampling which divided the sample space into more than one subspace. By analyzing and comparing the two random event's probability of obtaining the specific objectives at least one time among repeatedly independent random sampling from those models, the paper proves that the partition sampling model's probability is greater than the overall random sampling model's probability permanently when the amount of specific objective in the collectivity is 1 or 2.%为了增大基于种群操作的搜索技术在有限时间内捕捉到决策空间中的特定目标的概率,基于古典概率模型建立不划分的随机抽样模型和划分成多个子区域的随机抽样模型(简称划分模型),分析比较了两个模型分别进行多次独立随机抽样至少抽中1次特定目标的概率,并证明:当总体中特定目标的数量为1或2时,划分模型抽中特定目标的概率恒大于不划分模型的概率.
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