The Identification of Poverty Alleviation Targets Based on the Multiple Hybrid Decision-Making Algorithms

The poverty alleviation problem as one of the social evaluation applications has long been a major focus of social problems. As the basis and starting step of the poverty alleviation project, it is crucial to accurately identify the targets of poverty alleviation. Therefore, first of all, it is necessary to establish a scientific and reasonable indicators system and then evaluate all the indicator values respectively. However, in the process of data evaluation, we found that it is often hard to decide the unique valuation for some indicators because of the hesitation among different possible valuations in the mind. Different from traditional algorithms only using a single indicator valuation, the paper uses Pythagorean fuzzy sets (PFSs) to keep possible valuations from the positive and negative aspects and it can overcome the hesitation in the data evaluation process to a certain extent. The paper considers the problem of identifying the poverty alleviation targets as a multi-criteria decision making (MCDM) problem and then proposes a modified algorithm to solve the problem on the basis of several traditional algorithms. The algorithm work well and can obtain the maximum group utility and the minimum individual regret at the same time in the following experiments. The optimal poverty alleviation targets have been found and the poverty ranking list has also been obtained through the algorithm.