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现有求解网络计划资源优化的方法中,解析法不能解决大型复杂网络优化问题,启发式方法过多依赖具体问题、求解效率低,遗传算法生成新一代优化解种群依据的三个算子的实现参数选择,大部分依靠经验并严重影响解的品质,粒子群算法存在大型网络计划资源优化计算量过大和缺少大型网络计划资源优化算例问题.借助设计网络计划时间参数的计算机算法、建立评价函数、设计进化方程等基础工作,选择与工作开始时间相关的变量作为粒子空间位置,用蒙特卡洛方法和限制条件优化初始粒子群,设置可行解范围,用二维动态数组解决大型网络计划资源优化运行image超限问题,通过粒子群算法进化,寻求大型网络计划资源优化解,算例表明基于粒子群算法的大型网络计划资源优化效果明显,粒子群算法参数分析表明:粒子群算法的参数会影响网络计划资源优化结果,而且初始粒子群限制条件和优化目标设置的影响程度较大.
Among the existing methods for solving network planning resource optimization, analytic method can not solve the problem of large-scale complex network optimization. The heuristic method is too dependent on specific problems, the efficiency is low and the generation of three operators based on genetic algorithm to generate new generation of optimal solution population Most parameters depend on the experience and seriously affect the quality of the solution.Particle swarm optimization has the problem of large computational resources of large-scale network planning optimization and lack of optimization of large-scale network planning resources.Based on the computer algorithm of designing network planning time parameters, the evaluation function , Design the evolutionary equation and other basic work, select the variables associated with the start time of the work as the particle space position, using Monte-Carlo methods and constraints to optimize the initial particle swarm, set feasible solution range, with two-dimensional dynamic array to solve large network planning resource optimization The problem of image overrun is solved by using particle swarm optimization algorithm. The results show that the optimization of large scale network planning resource based on particle swarm optimization is significant. The parameter analysis of particle swarm optimization algorithm shows that the parameters of particle swarm optimization will affect Network Planning Resource Optimization Results, however The initial particle swarm optimization objectives and constraints set by a large degree of influence.