Abstract:
Abstract: To address the low adaptability of existing resource allocation algorithms for multi-agent systems in dynamic environments and their lack of fairness in resource distribution， this paper proposes a resource adaptive allocation algorithm for multi-agent systems oriented to dynamic demands. The proposed algorithm enhances resource utilization and fairness while optimizing allocation efficiency and system load balancing. A resource adaptive allocation model is constructed by incorporating the heterogeneity of resources and the dynamic nature of task demands in multi-agent systems. Through multi-stage optimization， the algorithm improves adaptability to dynamic environments and employs multi-objective optimization to balance resource allocation fairness and utilization. First， a fairness-based allocation strategy is used to select the set of tasks to be assigned. Then， inspired by swarm intelligence optimization， a Decentralized Search and Competitive Optimization （DSACO） algorithm is introduced to iteratively determine resource allocation schemes through multi-stage resource distribution and optimization. Finally， tasks are automatically assigned to corresponding agents based on the determined allocation schemes. Comparative simulation results demonstrate that， compared to existing algorithms， the proposed algorithm achieves not only fair resource allocation but also improves resource utilization and allocation efficiency. It exhibits strong adaptability to dynamic environments， providing an effective solution to the resource allocation challenges faced by multi-agent systems in complex scenarios.

Key words: Key words: dynamic demands, multi-agent, resource allocation, multi-objective optimization