Two different Hybrid Particle Swarm Optimization methods were proposed. In HPSO NO.1, mounting climbing operation was introduced to the global optimal value of each iterative.
设计了两种混合粒子群算法，对每代群体中的全局极值引入爬山操作构成混合PSO方案一，对每个粒子进行爬山操作构成混合PSO方案二。
参考来源 - 基于混合粒子群算法的物流配送车辆路径问题的研究·2,447,543篇论文数据，部分数据来源于NoteExpress
For complex functions with high dimensions, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima.
对于高维复杂函数，一般粒子群优化算法收敛速度慢，易早熟收敛。
The result shows that particle swarm optimization performs better in this case, which implies that the two methods traverse the problem hyperspace differently.
优化结果说明在这个例子中粒子群优化有更好的表现，这也说明两种方法在问题的多维空间中搜索最优解的方式不同。
Particle swarm optimization (PSO) algorithm is one of the most powerful methods for solving unconstrained and constrained global optimization problems.
粒子群优化算法(PSO)是一种有效的随机全局优化技术。
应用推荐