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- W2883463611 abstract "In the real world, there exist numerous multi-objective optimization problems (MOPs), and some representative multi-objective optimizers are proposed to meet the challenge. In this paper, an improved multi-objective fireworks algorithm (iMOFA) is presented to solve some complicated continuous MOPs. There are three significant features employed in iMOFA. Firstly, an initialization approach of homogenization with randomization is used to generate an even distributed initial population. Secondly, a method of fine-grained controlling radius is adopted to improve the search efficiency. In addition, a strategy of simplified k-nearest neighbor is utilized to maintain the diversity of external archive. Our iMOFA is compared with the other four multi-objective evolutionary algorithms (MOEAs) using IGD performance indicator on 12 benchmark MOP instances, and the numerical results show that the proposed iMOFA has the overall performance advantages in convergence and diversity." @default.
- W2883463611 created "2018-08-03" @default.
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- W2883463611 date "2018-01-01" @default.
- W2883463611 modified "2023-09-25" @default.
- W2883463611 title "An Improved Multi-objective Fireworks Algorithm" @default.
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- W2883463611 doi "https://doi.org/10.1007/978-981-13-1648-7_18" @default.
- W2883463611 hasPublicationYear "2018" @default.
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