协同震荡搜索混沌粒子群求解资源受限项目调度问题  

Cooperative shock search particle swarm optimization with chaos for resource-constrained project scheduling problems

作　　者：戴月明[1] 汤继涛[1] 纪志成[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机应用》2014年第6期1798-1802,共5页journal of Computer Applications

基　　金：国家863计划项目(2013AA040405);江苏省产学研联合创新基金资助项目(BY2012055)

摘　　要：针对求解资源受限项目调度问题(RCPSP),提出了协同震荡搜索混沌粒子群(CSCPSO)算法。算法围绕种群粒子吸引子建立双向协同震荡搜索机制,该机制一方面使粒子向吸引子收敛,另一方面使粒子震荡调整自身与吸引子相邻维度大小关系不一致的维度,提升算法的搜索精度和种群的多样性。项目调度采用基于粒子的拓扑排序和串行项目进度生成机制,保证项目调度解决方案满足资源约束和紧前约束。采用具体算例对算法进行检验,结果表明该算法在求解RCPSP的精度和稳定性方面表现更优。For Resource-Constrained Project Scheduling Problems （RCPSP）, the Cooperative Shock search Particle Swarm Optimization with Chaos （CSCPSO） was proposed. On the basis of particle attractor, a bidirectional cooperation shock search mechanism was established in the algorithm to enhance the search accuracy and diversity of population. The particles converged to particle attractor, meanwhile they adjusted the dimensions whose adjacent relationship were inconsistent with attractor＇s by shock search in the mechanism. Combined with topological sorting based on particles and serial schedule generation scheme, the gotten scheduling scheme could meet the project schedule constraints of resource and precedence relations. The tests on specific examples show that the proposed algorithm can get higher accuracy and better stability for RCPSP.

关 键 词：协同震荡搜索 混沌 粒子群优化算法 拓扑排序 资源受限项目调度问题 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]