Journal of Bionic Engineering ›› 2025, Vol. 22 ›› Issue (2): 866-883.doi: 10.1007/s42235-025-00653-4
An Improved Multi-objective Artificial Hummingbird Algorithm for Capacity Allocation of Supercapacitor Energy Storage Systems in Urban Rail Transit
Xin Wang1,2; Jian Feng1; Yuxin Qin3
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1 College of Electrical and Information Engineering, HunanUniversity of Technology, Zhuzhou 412007, Hunan, China
2 Hunan Engineering Research Centre of Electrical Drive andRegenerative Energy Storage and Utilization,Zhuzhou 412007, Hunan, China 3 School of Computer Science, University of Glasgow,Glasgow G12 8QQ, Scotland, UK
An Improved Multi-objective Artificial Hummingbird Algorithm for Capacity Allocation of Supercapacitor Energy Storage Systems in Urban Rail Transit
Xin Wang1,2; Jian Feng1; Yuxin Qin3
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1 College of Electrical and Information Engineering, HunanUniversity of Technology, Zhuzhou 412007, Hunan, China
2 Hunan Engineering Research Centre of Electrical Drive andRegenerative Energy Storage and Utilization,Zhuzhou 412007, Hunan, China 3 School of Computer Science, University of Glasgow,Glasgow G12 8QQ, Scotland, UK
摘要： To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.