A new particle swarm algorithm with dynamically changing inertia weight (DCW) is presented to solve the problem that the linearly decreasing weight (LDW) of the particle swarm algorithm cannot adapt to the complex and nonlinear optimization process.
并针对惯性权值线性递减粒子群算法(LDW )不能适应复杂的非线性优化搜索过程的问题,提出了一种动态改变惯性权的自适应粒子群算法(DCW )。
参考来源 - 基于粒子群优化的神经网络在药品管理中的应用·2,447,543篇论文数据，部分数据来源于NoteExpress
使用四个不同类型基准函数测试结果表明，新型算法比全局惯性权值算法性能更好。
The results illustrate that the new PSO has higher performance than the PSO with global inertia weight.
由于标准粒子群优化(PSO)算法把惯性权值作为全局参数，因此很难适应复杂的非线性优化过程。
The standard particle Swarm optimization (PSO) algorithm cannot adapt to the complex and nonlinear optimization process, because the same inertia weight is used to update the velocity of particles.
惯性权值作为粒子群算法的一个全局参数，能够方便地控制算法的搜索能力和收敛速度，在算法运行过程中具有重要的作用。
As a global parameter of PSO, inertia weight can easily control algorithm of search ability and convergence speed, and plays an important role of operation process in algorithm.
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