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

Authors

DOI:

https://doi.org/10.4108/eai.22-10-2021.171597

Keywords:

heterogenous multi-dimension targeted poverty alleviation, intuitive fuzzy set, genetic algorithm, Pareto solution

Abstract

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.

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Published

22-10-2021

How to Cite

1.
Shi Y, Shi Q, Mou X. Intuitionistic Fuzzy Set Similarity Degree Based on Modified Genetic Algorithm for Solving Heterogenous Multi-dimension Targeted Poverty Alleviation Data Scheduling Problem. EAI Endorsed Scal Inf Syst [Internet]. 2021 Oct. 22 [cited 2024 Nov. 14];9(35):e3. Available from: https://publications.eai.eu/index.php/sis/article/view/367