项目优化调度的病毒协同进化遗传算法

针对次序约束和资源约束的多模式项目调度问题提出了一种病毒协同进化遗传算法,并提出了解的编码、选择、交叉、变异和病毒感染操作等.算法用于求解项目活动的一个最优调度顺序和资源模式以使项目的成本最低,其操作特点是既可以通过遗传操作在父子代群体之间纵向传播进化基因进行全局搜索,又可以通过病毒感染操作在同一代群体内横向传播进化基因进行局部搜索.利用模板理论对算法的性能进行了分析.理论分析和实验结果表明,算法的搜索性能优于一般的遗传算法.算法对于不同优化目标的多模式项目调度问题可以同时求得一个满足次序约束的项目活动的最优调度顺序和满足资源约束的最优资源模式.

A Virus Coevolution Genetic Algorithm for Project Optimization Scheduling

In this paper, a virus coevolution genetic algorithm (multi-mode project scheduling-virus co-evolution genetic algorithm, MPS-VEGA) for the precedence and resource constrained multi-mode project scheduling problem is presented, and the encoding of the solution and the operators such as selection, crossover, mutation and virus_infection are given. MPS-VEGA is used to obtain the optimal scheduling sequences and resource modes for the activities of the project so that the project cost is minimized, which can transmit evolutionary genes not only between parent and child generations vertically by the genetic operators but also in the same generation horizontally by the virus_infection operator so as to perform a global search and a local search, respectively. The schema theorem is adopted to analyze the performance of MPS-VEGA. The theoretical analysis and experimental results show that the MPS-VEGA outperforms the GA. For the multi-mode project scheduling problem with different optimization objectives, MPS-VEGA can simutaneously give standard the optimal scheduling sequences subject to the precedence constraints and the optimal resource modes for the activities of the project.