【摘要】 针对现有多智能体系统资源分配算法对动态变化环境的适应性较低和资源分配公平性欠缺等问题，提出一种面向动态需求的多智能体系统资源自适应分配算法，以提高资源利用率与公平性，同时优化资源分配效率和系统负载均衡性。结合多智能体系统中资源异质性和任务需求动态性的特征，构建资源自适应分配模型。通过多阶段优化提高算法对动态环境的适应性，同时结合多目标优化均衡资源分配的公平性和资源利用率。算法首先运用公平分配策略，选择待分配的任务集合；然后，结合群智能优化算法思想，提出分散搜索与竞争优化算法（Decentralized Search And Competitive Optimization,DSACO），通过多阶段资源分配与优化，可自适应确定资源分配方案；最后，根据资源分配方案自动将任务分配至对应的智能体。对比仿真实验结果表明，相比已有算法，本文提出的算法不仅可实现资源的公平分配，还可提高资源利用率和分配效率，具备较强的动态适应能力，为多智能体系统中复杂环境下的资源分配问题提供有效的解决方案。更多还原

【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.更多还原