TY - JOUR

T1 - Predefined-time distributed constrained multi-conflicting objective optimization with nonlinear uncertainty over directed graph

AU - Jiang, He

AU - He, Junlong

AU - Chen, Sen

AU - Deng, Zhenhua

N1 - This work was supported by the Natural Science Basic Research Program of Shaanxi Province (Grant No. 2025JC-YBQN-035), the Hunan Provincial Natural Science Foundation of China (Grant No. 2024JJ4067), the Natural Science Foundation of Changsha (Grant No. kq2402224), and the T-Flight Laboratory in ShanXi Provincial (Grant No. GSFC2024NBKY05).

PY - 2025/12

Y1 - 2025/12

N2 - This paper addresses the constrained multi-conflicting objective optimization (MCOO) problem for multi-agent systems under strong nonlinear uncertainty over directed graph. Each agent is subject to multiple conflicting local objectives. To enable agents to autonomously seek the Pareto optimality of the MCOO problem, three distributed algorithms are developed. First, by utilizing the online updating weighted Lp preference index, the MCOO problem is reformulated into a single-objective optimization problem, and two essential parameters are determined by solving auxiliary optimization subproblems. Next, to actively eliminate and compensate for the impact of strong nonlinear uncertainty in three optimization problems, three reduced-order extended vector observers are utilized. By the three proposed algorithms employing time-based generator, state feedback, and disturbance compensation, all agents converge to an arbitrarily small neighborhood of the Pareto optimality within predefined time, although strong nonlinear uncertainty exists and the predefined time can be set arbitrarily. Furthermore, simulation example verifies the effectiveness of the proposed algorithms.

AB - This paper addresses the constrained multi-conflicting objective optimization (MCOO) problem for multi-agent systems under strong nonlinear uncertainty over directed graph. Each agent is subject to multiple conflicting local objectives. To enable agents to autonomously seek the Pareto optimality of the MCOO problem, three distributed algorithms are developed. First, by utilizing the online updating weighted Lp preference index, the MCOO problem is reformulated into a single-objective optimization problem, and two essential parameters are determined by solving auxiliary optimization subproblems. Next, to actively eliminate and compensate for the impact of strong nonlinear uncertainty in three optimization problems, three reduced-order extended vector observers are utilized. By the three proposed algorithms employing time-based generator, state feedback, and disturbance compensation, all agents converge to an arbitrarily small neighborhood of the Pareto optimality within predefined time, although strong nonlinear uncertainty exists and the predefined time can be set arbitrarily. Furthermore, simulation example verifies the effectiveness of the proposed algorithms.

KW - Directed graph

KW - Distributed multi-conflicting objective optimization

KW - Disturbance compensation

KW - Multi-agent systems

KW - Predefined-time

UR - https://www.mendeley.com/catalogue/c6846e7e-1ea8-3a5c-aae2-25564e8cf3cd/

U2 - 10.1016/j.ins.2025.122512

DO - 10.1016/j.ins.2025.122512

M3 - Article

VL - 720

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

M1 - 122512

ER -