An improved multi-objective brainstorming algorithm with the application of rapeseed germination characteristics optimization

Yang Yang, Bai Lin Liu, Jiang Wang, Yu Geng Chen, Yi Lin Ren*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

As one of the most important raw materials for daily edible oil, the annual production and quality of oilseed rape have a great impact on the daily lives of people and the development of the country. The germination quality of rapeseed directly affects the future yield and quality of oilseed rape, and seed germination vigor (SGV) and chlorophyll content (CC) are important factors affecting the quality of rapeseed germination. It is very necessary to make the rapeseeds have optimal germination characteristics and seed germination vigor at the early stage of germination by controlling the combination of each decision variable at the segmented harvest. The two objectives are coupled with each other to form a complex multi-objective optimization problem, which is important in the current research field of the rapeseed problem. To solve this problem, a modified brainstorming optimization algorithm based on OPTICS clustering, named MOPCBSO, is proposed in this study. The algorithm uses optics clustering as a new clustering method, chaotic mutation as a new updating method for individuals, and introduces an elite strategy and a tracking strategy. 19 benchmark test functions and nonparametric tests are adopted to examine the performance of the MOPCBSO compared with other optimization algorithms. The results show that the proposed algorithm has significant advantages over other algorithms in terms of convergence and comprehensiveness, demonstrating the effectiveness of the algorithm improvement. To optimize rapeseed germination characteristics, mathematical models are built, in which, cut-down time, after-ripening time and stubble height are selected as decision variables, while chlorophyll concentration and seed germination vigor in seeds as the target characteristics. The data of oliseed rape field trials were collected from 2020 to 2021 in Huanggang City, Hubei Province. Finally, the optimal solution for the germination characteristics of rapeseeds was obtained by the MOPCBSO, which provided an effective basis for the subsequent rapeseed production experiments.

Original languageEnglish
Article number107865
JournalComputers and Electronics in Agriculture
Volume209
DOIs
Publication statusPublished - Jun 2023
Externally publishedYes

Keywords

  • Agricultural products
  • Brainstorm optimization
  • Multi-objective optimization
  • Rapeseed
  • Support vector machines regression

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