Genetic algorithm-based secure cooperative control for high-order nonlinear multi-agent systems with unknown dynamics

• Published in: Journal of cloud computing : advances, systems and applications systems and applications, 2024-12, Vol.13 (1), p.1-17, Article 1
• Main Authors: Wang, Xin, Yang, Dongsheng, Dolly, D Raveena Judie, Chen, Shuang, Alassafi, Madini O., Alsaadi, Fawaz E., Lyu, Jianhui
• Format: Article
• Language: English
• Subjects: Adaptive control; Cloud computing; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Cooperative control; Edge-cloud computing; Genetic algorithm; Genetic algorithms; High-order nonlinear model; Information Systems Applications (incl.Internet); Iterative methods; Multi-agent systems; Multiagent systems; Nonlinear control; Nonlinear systems; Robotics; Secure cooperative control; Security and privacy issues for artificial intelligence in edge-cloud computing; Software Engineering/Programming and Operating Systems; Solution space; Special Purpose and Application-Based Systems; Task complexity; Unknown dynamics
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-023-00532-5

Research has recently grown on multi-agent systems (MAS) and their coordination and secure cooperative control, for example in the field of edge-cloud computing. MAS offers robustness and flexibility compared to centralized systems by distributing control across decentralized agents, allowing the system to adapt and scale without overhaul. The collective behavior emerging from agent interactions can solve complex tasks beyond individual capabilities. However, controlling high-order nonlinear MAS with unknown dynamics raises challenges. This paper proposes an enhanced genetic algorithm strategy to enhance secure cooperative control performance. An efficient encoding method, adaptive decoding schemes, and heuristic initialization are introduced. These innovations enable compelling exploration of the solution space and accelerate convergence. Individual enhancement via load balancing, communication avoidance, and iterative refinement intensifies local search. Simulations demonstrate superior performance over conventional algorithms for complex control problems with uncertainty. The proposed method promises robust, efficient, and consistent solutions by adapting to find optimal points and exploiting promising areas in the space. This has implications for securely controlling real-world MAS across domains like robotics, power systems, and autonomous vehicles.