The practical applications of engineering designs are often concerned by the presence of interrelated decisions. Naturally, these decisions are induced by solving many conflicting and incommensurable objectives. To deal with the interlinked decisions as well as the paradox natures among objectives, this paper presents an integrated approach based on dynamic programming approach (DPA) and intuitionistic fuzzy set (IFS) denoted as DPA-IFS for solving multi-objective optimization problem (MOOP). In DPA-IFS, the principle of DPA aims to generate an efficient solution of MOOP. In contrast, IFS aims to handle conflicting natures among the objective functions by means of the satisfaction (maximization the degree of membership) and dissatisfaction (minimization the degree of non-membership) concepts. The illustration is investigated by numerical illustrations taken from the literature. Furthermore, a new closeness strategy-based distance function is introduced to measure the worth of a satisfactory solution. The proposed methodology is validated on the IEEE 30-bus with six generators as a real test paradigm in the electrical power system. The results obtained by the DPA-IFS show the superior results than those obtained by different approaches.

相互关联的决策的存在常常关系到工程设计的实际应用。当然，这些决定是通过解决许多相互矛盾且无法衡量的目标而得出的。为了解决目标之间的相互联系的决策以及矛盾性，本文提出了一种基于动态规划方法（DPA）和直觉模糊集（IFS）的集成方法，称为DPA-IFS，用于解决多目标优化问题（MOOP）。 ）。在DPA-IFS中，DPA的原理旨在生成有效的MOOP解决方案。相反，IFS旨在通过满足（最大化成员资格程度）和不满意（最小化非成员资格程度）概念来处理目标函数之间的冲突性质。通过从文献中获得的数字插图来研究该插图。此外，引入了一种新的基于距离策略的距离函数来衡量满意解决方案的价值。所提议的方法已在具有六台发电机的IEEE 30总线上得到验证，可以作为电力系统中的真实测试范例。通过DPA-IFS获得的结果显示出比通过不同方法获得的结果更好的结果。