欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对高维复杂约束优化问题，提出了一种基于平滑技术和一维搜索的粒子群算法（NPSO）。该算法使粒子的飞行无记忆性，结合平滑函数和一维搜索重新生成停止进化粒子的位置，增强了在最优点附近的局部搜索能力；定义了不可行度阈值，利用此定义给出了新的粒子比较准则，该准则可以保留一部分性能较优的不可行解微粒，使微粒能快速的找到位于约束边界或附近的最优解；最后，为了扩大粒子的搜索范围，引进柯西变异算子。仿真结果表明，对于复杂约束优化问题，算法寻优性能优良，特别是对于超高维约束优化问题，该算法获得了更高精度的解。
英文摘要：
A novel particle swarm optimization （NPSO） based on the smooth scheme and line search is proposed for solving complex constrained optimization problems. First, the inertia weight is set to zero, and the position of the particle whose evolution has stopped is produced by smooth scheme and line search. In this step, the local search ability is improved. Second, a new comparison strategy is proposed based on the new concept of infeasible threshold value. It can preserve some infeasible solutions with high quality and can make the particles reach the global optimal solutions located on or near the boundary of the feasible region quickly. Finally, the Cauchy mutation operator is introduced which can expand the search range. The simulation results show that the proposed algorithm is effective for complex constrained optimization problems, especially for the problems with high dimensions.
同期刊论文项目
同项目期刊论文
期刊信息
位置：