Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization - Li, Xue; Yang, Yixuan; Li, Zheng; He, Hui (International journal of cognitive computing in engineering, 2025)
•This study introduces a collaborative optimization strategy for railway signal control systems using the BBO algorithm, addressing performance bottlenecks in existing nonlinear control methods.•By constructing a mathematical model with multi-objective optimization, the research highlights improvements in train running speed and signal delay, enhancing overall operational efficiency.•Experimental results demonstrate that the BBO algorithm outperforms traditional optimization methods, leading to a 15% increase in average train speed and a 20% reduction in signal delay.•The proposed strategy ensures system stability and reliability, providing a robust framework for the intelligent development of railway transportation, ultimately enhancing safety and operational effectiveness. This study focuses on exploring collaborative optimization strategies for a nonlinear control system of railway signals based on the BBO algorithm. Currently, the railway signal control system faces performance bottlenecks such as response lag and local optima due to parameter coupling when dealing with multi-objective optimization problems like train operating speed and signal delays. Traditional optimization methods struggle to achieve global collaborative regulation under complex operating conditions. Therefore, there is an urgent need to introduce efficient intelligent algorithms to enhance the system's real-time capabilities and reliability. The research constructs a mathematical model with multiple objective constraints, accurately identifies the adaptation shortcomings of the existing system in dynamic scenarios, and then employs a Biogeography-Based Optimization (BBO) algorithm for global optimization of control parameters. Specifically, it sets a population size of 50, a maximum number of iterations of 200, a migration rate dynamically adjusted between 0.6-0.9, and an adaptive mutation rate of 0.01-0.05, using root mean square error and response time as performance evaluation metrics for parameter optimization. Experimental data show that compared to traditional methods, this strategy can increase the average operating speed of trains by 15%, reduce signal delays by 20%, and improve system robustness indicators by 18.5%, achieving a collaborative enhancement of efficiency and safety while ensuring stable operation, thus providing an engineering-valued solution for the intelligent upgrade of railway transport.