Large power system contains many generating units, and many factors need to be considered during operation. The unit combination optimization is a nonlinear large-scale optimization problem with multi-objective and multi-constraints, and the existing methods have many shortcomings. Artificial bee colony algorithm has good performance in solving nonlinear optimization problems, but it has some disadvantages such as low optimization efficiency and local extremum. To solve these problems, an improved artificial bee colony algorithm is proposed. The algorithm introduces variable field of vision, adjusts the initial solution and selection strategy, and combines with mutation operation in genetic algorithm. A multi - objective optimization model considering economy and environmental protection is constructed. In order to solve the problem that the calculation time is too long due to the expansion of the unit scale, a phased optimization method is adopted, and the improved algorithm is applied to the start-stop scheduling stage. After determining the start-stop state of the unit, the mixed integer programming method is used for load distribution. For unit contains up to 1000 units of large power grid optimization examples are simulated experiment, the experimental results show that the improved optimization algorithm convergence and global search ability is improved, greatly reduces the computation time of the mass unit combination, multiple objective conditions have also made the ideal results, verify the effectiveness of the proposed method.

Yaoyao Han, Yongli Zhu. Research on improved artificial bee colony algorithm based on large-scale multi-objective group combination optimization. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 5: 26-32. https://doi.org/10.25236/AJCIS.2022.050504.

[1] SONG Yang. Intelligent Improvement and Optimization of Ant Colony Algorithm Based on Genetic Algorithm [J]. Network Security Technology and Application,2020(10):71-73.

[2] FU Zheng. Application of meta-heuristic algorithm in image processing [D]. Nanjing University of Posts and Telecommunications, 2019.

[3] Shen DINGjun. Theoretical and practical research on Socialism with Chinese Characteristics in the New Era. Journal of jilin institute of education, 2019, 35(10): 167-170.

[4] Miao Tianyu, QIN Ruiping. History, Theory and Practice: Multidimensional Analysis of the meaning of Socialism with Chinese Characteristics [J]. Journal of Jinan Municipal Party School, 2019(04): 7-11.

[5] Zhang Pinghua. Research on improved artificial bee colony algorithm for discrete optimization problems [J]. Journal of hebei north university (natural science edition), 2019, 35(05): 13-18.

[6] Yao Ya, noble. Fuzzy clustering algorithm based on hybrid artificial bee colony [J]. Computer and digital engineering,2019,47(05):1072-1077.

[7] FU Jian. Multi-mode dispersion curve inversion of Rayleigh waves based on improved nonlinear algorithm [D]. Chang'an University, 2019.

[8] Jia KAI. Study on parallel integration learning between artificial bee colony algorithm and BP neural network [D]. Hefei University of Technology, 2019.

[9] Jia Kai, Ni Zhiwei, Li Jingming, Lu Yujia, Zhu Xuhui. Parallel Ensemble Learning Algorithm of BP Neural Network Based on Improved Binary Artificial Bee Colony and its Application [J]. Journal of systems science and mathematics, 2019, 39(03): 477-494.

[10] Li Dawei. The theory and practice of socialist rule of law culture with Chinese characteristics [J]. Legal System and Society, 2019(05): 5+36.

[11] Yuan Fuzhen. Contribution of Road Theory and Practice of Socialism with Chinese Characteristics to the World [N]. Jilin Party School Journal, 2018-12-15 (004).

[12] ZHU Bo. Research on improvement of artificial bee colony algorithm [D]. Yunnan University of Finance and Economics, 2018.

[13]. The world significance of the theory and practice of Socialism with Chinese characteristics [J]. Petroleum Political Industry Research, 2018(02): 43-46.

[14] [Liu W. Xi Jinping's Economic Thought of Socialism with Chinese Characteristics for a New Era is the logical unity of history and thought, theory and practice.] Social Sciences in China Universities, 2018(02): 15-20+9.]

[15] Tian T. Chaotic S-box structure and its application in image encryption [D]. Harbin Engineering University, 2017.

[16] Duan Qingyan. Spectrum Allocation Algorithm based on Intelligent Optimization [D]. Chongqing University, 2016.