As an uncertain programming model with multiple conflicting performance indices and bounded uncertain parameter(s), multiobjective interval number programming is a daunting topic in the fields of mathematics and intelligent optimization. Despite its comprehensive engineering application background, it is still open, and further studies are needed on basic theory, model transformation and intelligent optimizers. Therein, this work not only gropes a new shortcut to tackling one such model, but also proposes a novel multiobjective interval number immune optimization algorithm. The intrinsic solution relation between the model and a related natural interval extension one is discovered in terms of the new concept of optimal-value vector solution, by which a fast interval nondominated sorting approach is acquired. The algorithm mainly consists of population division, proliferation, evolution, selection and memory update, in which a co-evolutionary mechanism is designed to promote the current population to move quickly towards the Pareto front with the assistance of the sorting approach and an external archive set. The algorithm's resource consumption depends mainly on the archive's size. Comparative experiments have validated that the optimizer can effectively perform well over the compared approaches and is significantly superior to them with regard to efficiency.

作为具有多个相互矛盾的性能指标和有界不确定参数的不确定编程模型在数学和智能优化领域，多目标区间数编程是一个艰巨的课题。尽管具有广泛的工程应用背景，但它仍然是开放的，并且需要对基础理论，模型转换和智能优化器进行进一步的研究。其中，这项工作不仅为解决这种模型寻找了一条新的捷径，而且提出了一种新颖的多目标区间数免疫优化算法。根据最优值向量解的新概念，发现了模型与相关的自然区间扩展一个之间的内在解关系，从而获得了一种快速区间非支配排序方法。该算法主要包括种群划分，扩散，进化，选择和记忆更新，在这种方法中，设计了一种协同进化机制，以借助分类方法和外部档案集来促进当前人口迅速向帕累托前沿发展。该算法的资源消耗主要取决于档案的大小。对比实验已经证实，优化器可以有效地胜过所比较的方法，并且在效率方面明显优于它们。