社会协作的多智能体进化

提出了一种新的求解函数优化的算法．借鉴社会协作机制，定义可信任度表示智能体的历史活动信息，控制智能体间的相互作用; 引入“熟人关系网”模型构建和更新智能体的局部环境，利用多智能体之间的协作特性来加快算法收敛速度; 并构造了非一致变异算子保证智能体种群的多样性．仿真实验结果表明，与性能优越的多智能体遗传算法相比，该算法能以更少的函数评价次数找到精度更高的最优解．

Social cooperation based multi-agent evolutionary algorithm

A Social Cooperation based Multi-Agent Evolutionary Algorithm (SCMAEA) which integrates the social cooperation mechanism and multi-agent evolution for numerical optimization is proposed. Using the social cooperation mechanism, trust degree, which denotes the historical information for agents, is defined to control the interaction between agents. At the same time, the ‘acquaintance net model is imported to construct and update the local environment of the agent. It improves the convergence rate by the cooperation characteristic of agents. Furthermore, adopting the non-uniform mutation operation improves the searching for optimal solutions in the local region and assures the diversity of the solution. Simulation results show that compared with the multi-agent genetic algorithm, the social cooperation based multi-agent evolutionary algorithm can find the optima by a smaller number of function evaluations.