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A reference points and intuitionistic fuzzy dominance based particle swarm algorithm for multi/many-objective optimization

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Abstract

Intuitionistic Fuzzy Sets is one of the most influential extension and development of Zadeh’s fuzzy set theory. It has strong performance in dealing with uncertain information, while taking into account information on membership degree, non-membership degree and hesitation degree. In this paper, a new loose Pareto dominant relationship named intuitionistic fuzzy dominance is adopted to research multi/many-objective particle swarm optimization problems. Particle swarm optimization (PSO) with double search strategy is employed to update the population to enhance the exploitation and exploration capability of particle in the objective space, especially high-dimensional objective space. In addition, the uniformly distributed reference points are used to balance the convergence and diversity of the algorithm. The proposed algorithm has been compared with four recent multi-objective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary algorithms on 16 benchmark MOPs with 3, 5,8,10 and 15 objectives, respectively. The simulation results show that the proposed algorithm has better performance on most test problems.

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Notes

  1. 1. https://github.com/BIMK/PlatEMO

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Acknowledgments

This work was supported in part by National Key Research and Development Program Projects of China (Grant No. 2018YFC1504700) and project of Natural Science Foundation in Shaanxi Province (Grant No. 2018JM6029).

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Authors and Affiliations

  1. School of Information Technology and Software, Northwest University, Xi’an, 710127, China

    Wusi Yang, Li Chen & Yi Wang

  2. Xi’an Center of Geological Survey, Key Laboratory of Loess Landslide, China Geological Survey, Xi’an, 710054, China

    Maosheng Zhang

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Correspondence to Yi Wang.

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Yang, W., Chen, L., Wang, Y. et al. A reference points and intuitionistic fuzzy dominance based particle swarm algorithm for multi/many-objective optimization. Appl Intell 50, 1133–1154 (2020). https://doi.org/10.1007/s10489-019-01569-3

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