Intuitionistic Fuzzy Set Similarity Degree Based on Modified Genetic Algorithm for Solving Heterogenous Multi-dimension Targeted Poverty Alleviation Data Scheduling Problem

EAI Endorsed Transactions on Scalable Information Systems

article

Yang Shi¹  Qingwu Shi¹  Xiaofeng Mou¹ 

[1] College of Information and Electronic Technique, Jiamusi University

关键词: heterogenous multi-dimension targeted poverty alleviation;    intuitive fuzzy set;    genetic algorithm;    Pareto solution;   

DOI  :  10.4108/eai.22-10-2021.171597

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

Targeted poverty alleviation is a proposed concept in comparison with extensive poverty alleviation. It mainly aims at the poverty situation of different rural areas and farmers in China and adopts scientific and reasonable methods to carry out targeted assistance policies. It executes accurate management for the targeted poverty alleviation. This way for poverty alleviation is more precise. In the research of heterogenous multi-dimension targeted poverty alleviation data scheduling, the multi-dimension processing is very important. In this paper, we propose an intuitionistic fuzzy set similarity degree based on modified genetic algorithm for solving heterogenous multi-dimension targeted poverty alleviation data scheduling problem. In the proposed algorithm, the reference solution and Pareto solution are mapped to the reference solution intuitive fuzzy set and Pareto solution intuitive fuzzy set respectively. The intuitionistic fuzzy similarity between two sets is calculated to judge the quality of Pareto solution. The similarity value of intuitionistic fuzzy sets is used to guide the evolution of multi--dimension genetic algorithm. The results show that the proposed algorithm can effectively solve the problem of heterogenous multi-dimension targeted poverty alleviation data scheduling, especially, in large scale problems.

【 授权许可】

CC BY