sis 18: e15

Research Article

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

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  • @ARTICLE{10.4108/eai.22-10-2021.171597,
        author={Yang Shi and Qingwu Shi and Xiaofeng Mou},
        title={Intuitionistic Fuzzy Set Similarity Degree Based on  Modified Genetic Algorithm for Solving Heterogenous  Multi-dimension Targeted Poverty Alleviation Data Scheduling Problem},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={10},
        keywords={heterogenous multi-dimension targeted poverty alleviation, intuitive fuzzy set, genetic algorithm, Pareto solution},
        doi={10.4108/eai.22-10-2021.171597}
    }
    
  • Yang Shi
    Qingwu Shi
    Xiaofeng Mou
    Year: 2021
    Intuitionistic Fuzzy Set Similarity Degree Based on Modified Genetic Algorithm for Solving Heterogenous Multi-dimension Targeted Poverty Alleviation Data Scheduling Problem
    SIS
    EAI
    DOI: 10.4108/eai.22-10-2021.171597
Yang Shi1, Qingwu Shi1,*, Xiaofeng Mou1
  • 1: College of Information and Electronic Technique, Jiamusi University, Jiamusi, 154007 China
*Contact email: shiqingwu@126.com

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.