论文部分内容阅读
大型网络计划费用优化对科学有效地进行工程项目进度管理具有重要意义,但大型网络计划费用优化随工作个数增加而约束方程和计算量骤增,成为数学和计算机科学领域至今未解决的难题.借助建立评价函数、设计进化方程、设计网络计划时间参数的计算机算法等基础工作,选择工作持续时间作为粒子空间坐标并设置可行解范围,用蒙特卡洛方法和限制条件优化初始粒子群,用二维动态数组解决大型网络计划粒子群算法优化运行image超限问题,成功求解有61个工作的大型网络计划费用优化算例.因此,经过特定设计的粒子群算法是微机和有限的计算时间条件下求解大型网络计划费用优化问题的一个有效方法.
The optimization of large-scale network planning costs is of great significance for scientifically and effectively managing the progress of engineering projects. However, the optimization of large-scale network planning costs has become an unsolved problem in math and computer science with the increase of the number of work while the constraint equations and calculation increases sharply. With the help of establishing the evaluation function, designing the evolutionary equation, designing the computer algorithm of the network planning time parameter and other basic work, choosing the working duration as the particle space coordinate and setting the feasible solution range, optimizing the initial particle swarm by Monte Carlo method and the restriction condition, Dimensional Dynamic Particle Swarm Optimization Particle Swarm Optimization for Large-Scale Network Planning To overcome the problem of image overrun, we successfully solve 61 large-scale network planning cost optimization cases. Therefore, the particle swarm optimization (PSO) An efficient way to solve the optimization problem of large-scale network plan.