一种改进型粒子群算法求解资源优化配置问题

An Improved Particle Swarm Optimization Algorithm for Solving Resource Allocation Problems

针对粒子群算法在求解优化问题时难以兼顾收敛精度与收敛速度这一问题,提出对目标的惯性权重进行修正和引入随着惯性权重变化的惯性学习因子的方法,该算法充分利用了上一代速度与位置、自我认知和群体间信息共享3部分内容,来影响算法的优化结果,提高了算法的全局和局部的搜索能力.最后将改进的粒子群算法应用于工程项目中的资源优化配置问题中,证明了该算法的有效性.

The particle swarm optimization in solving optimization problems is difficult to balance the convergence accuracy and the convergence speed,so the method of correcting the aiming inertia weight and introducing the inertia learning factor with changing of inertia weight is put for-ward.The algorithm makes full use of speed and position,self-cognition and sharing information among groups of the last generation to influence the results of optimization algorithm and improve the global and local search ability.Finally,the improved particle swarm optimization algorithm is applied to the resource optimized configuration of the project to demonstrate its effectiveness.