资源受限项目调度的多智能体文化演化算法

Multi-agent Cultural Evolutionary Algorithm for Resource-constrained Project Scheduling

结合文化算法的双层结构和多智能体进化算法的演化优势,提出一种求解资源受限项目调度问题的多智能体文化演化算法。算法设置了上层信仰空间和下层群体空间,各空间内智能体通过与其邻域进行竞争、合作操作及自学习操作来增加自身的能量,空间之间的交互是定期通过接受操作和影响操作采用同步传输方式来完成。通过对资源受限项目调度标准数据库PSPL IB中多个32、62、92、122工作的项目调度问题的仿真,结果表明:此算法不仅具有很好的收敛特性,而且运行速度快,是一种求解大规模调度问题的有效算法。

Combined the dual-evolution structure of Cultural Algorithm（CA） with the evolution advantage of multi-agent evolutionary algorithm,multi-agent cultural evolutionary algorithm（MACEA） is proposed to solve resource-constrained project scheduling problem.The algorithm sets the population space and belief space.The agent in each space increases its energy through the operations such as competition and cooperation with its neighborhood,and the self-study operation.The interaction of two spaces is by the acceptance operation and impact operation with synchronous transmission mode regularly.The simulation results of the standard PSPLIB problems with 32,62,92 and 122 jobs show that the algorithm not only possesses the global convergence but also the higher convergent speed.It is an effective algorithm for large scale scheduling problem.