中文摘要：

针对并行多任务环境下Agent联盟的生成问题，提出了基于多种群蚂蚁算法的Agent联盟生成策略．在该联盟生成策略中，种群内部蚂蚁相互合作，协调资源分配并完成相应任务；种群间蚂蚁进行资源竞争，协调解决并行多项任务间的资源冲突．同时，改进的信息素更新策略在综合考虑局部联盟收益和全局联盟收益的基础上提高了算法的全局搜索能力和生成联盟的质量．仿真实验结果表明，文中算法在多种典型条件下都能生成比现有算法更加高效的联盟结构．

英文摘要：

A multi-colony ant colony optimization （MCACO） is proposed for the coalition generation problem in the parallel multi-task environment. In this algorithm, the ants from the same colony cooperate to accomplish a task by reasonably allocating the resources, while those from different colonies scramble the resources and resolve the resource conflict among multiple parallel tasks. Moreover, a specially-designed pheromone update rule is applied to enhance the global search ability of MCACO and improve the generation quality of agent coalition by taking the tradeoff between the local optimization benefit and the global one. Simulation results indicate that the proposed rithm helps to generate more effective coalition in multiple benchmark environments.