@inproceedings{1f913a866b2c482c90abbaa4273da835,
title = "Multi-Objective Optimization for Swarm Tracking Problem Using the CMA-ES Algorithm",
abstract = "This paper studies the optimization method for tuning model parameters in the swarm-tracking task. First, a discretetime and linearized agent dynamics model is given, and a swarm control model is designed according to the swarm trajectory tracking task. Second, the order parameters reflecting the tracking performance are designed according to the multiple goals of the task. A series of normalization functions are used on the given order parameters, and then a multi-objective unified fitness function is obtained. Third, the unified fitness function is optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The result of numerical simulation shows that the swarm can track the trajectory consistently without collision. Compared to the model with manually designed parameters, the optimized swarm model achieves higher velocity correlation and lower tracking error.",
keywords = "CMA-ES, Evolutionary Algorithm, Multi-objective Optimization, Swarm Control",
author = "Yangqiaoyi Xiao and Yuanchen Zhao and Guibin Sun and Kexin Liu",
note = "Publisher Copyright: {\textcopyright} 2025 Technical Committee on Control Theory, Chinese Association of Automation.; 44th Chinese Control Conference, CCC 2025 ; Conference date: 28-07-2025 Through 30-07-2025",
year = "2025",
doi = "10.23919/CCC64809.2025.11178830",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5757--5762",
editor = "Jian Sun and Hongpeng Yin",
booktitle = "Proceedings of the 44th Chinese Control Conference, CCC 2025",
address = "美国",
}